U.S. patent application number 14/735998 was filed with the patent office on 2015-12-24 for visual experience map for media presentations.
The applicant listed for this patent is BrightSky Labs, Inc.. Invention is credited to Christopher Beard, Ann-Caryn Cleveland, Ian McCarthy, Sean M. White.
Application Number | 20150370806 14/735998 |
Document ID | / |
Family ID | 54869650 |
Filed Date | 2015-12-24 |
United States Patent
Application |
20150370806 |
Kind Code |
A1 |
White; Sean M. ; et
al. |
December 24, 2015 |
VISUAL EXPERIENCE MAP FOR MEDIA PRESENTATIONS
Abstract
Systems and methods for video editing and playback are provided.
In one implementation, a selected portion of a timeline for
navigating media content can be repositioned and resized by user
input actions received along various axes relative to the timeline.
In another implementation, a plurality of signals associated with
media content can be intelligently weighted based on user group
historical attributes to identify portions of interest in the media
content. In a further implementation, an experience map for media
content is provided in which a representative signature for the
content includes visual signal intensity representations and social
interest concentrations over the length of the content. In another
implementation, a subset of filters is determined for
recommendation to a user based on one or more attributes associated
with at least one of media content, the user, a group of users, or
a user device.
Inventors: |
White; Sean M.; (Mountain
View, CA) ; Beard; Christopher; (San Jose, CA)
; McCarthy; Ian; (Menlo Park, CA) ; Cleveland;
Ann-Caryn; (Pacifica, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
BrightSky Labs, Inc. |
Mountain View |
CA |
US |
|
|
Family ID: |
54869650 |
Appl. No.: |
14/735998 |
Filed: |
June 10, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62131455 |
Mar 11, 2015 |
|
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|
62047553 |
Sep 8, 2014 |
|
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62014203 |
Jun 19, 2014 |
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Current U.S.
Class: |
715/721 |
Current CPC
Class: |
G06F 16/438 20190101;
G11B 27/34 20130101; G06F 16/435 20190101; G06F 3/04883 20130101;
G06F 3/04845 20130101; G06F 16/9535 20190101; G06F 3/04842
20130101; G06F 16/447 20190101; G06F 16/955 20190101; G06F 3/04847
20130101; H04L 65/403 20130101; G11B 27/031 20130101; G06F 3/0482
20130101; G06F 3/0486 20130101; H04N 5/265 20130101; G06F 16/7867
20190101; G11B 27/102 20130101; G06Q 50/01 20130101; G11B 27/034
20130101; G11B 27/002 20130101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; H04L 29/06 20060101 H04L029/06; G06F 3/0482 20060101
G06F003/0482 |
Claims
1. A computer-implemented method comprising: providing a visual
representation of a timeline of a video wherein the timeline
comprises a plurality of different time positions in the video;
providing a visual representation of one or more signals along the
timeline of the video, wherein each signal representation comprises
a respective intensity of the signal over the time positions;
obtaining, for each of a plurality of users, an indication of
interest of the user in a respective portion of the video; and
providing a visual representation on the timeline of each
indication of interest.
2. The method of claim 1, wherein the visual representation of the
signals comprises a visual representation of a weighted sum of
signals.
3. The method of claim 1, wherein a particular indication of
interest comprises a comment, a like, a share, or a highlight.
4. The method of claim 1, further comprising determining a social
signal based on the indications of interest, wherein an intensity
of the social signal over a length of the video is based on a
concentration of the indications of interest over the length of the
video.
5. The method of claim 4, further comprising: receiving a second
video comprising a plurality of signals, each signal representing
an identifiable type of content over a length of the video; for at
least one of the signals, associating a weighting with the signal,
wherein the weighting is determined based at least in part on the
social signal; and identifying one or more portions of interest in
the second video based on the at least one signal weighting.
6. The method of claim 5, wherein the weighting is determined based
at least in part on the intensity of the social signal.
7. The method of claim 5, wherein the weighting is determined based
at least in part on indications of interest from a plurality of
videos.
8. The method of claim 1, wherein a particular signal represents
one of motion, sound, presence of faces, recognized faces,
recognized objects, recognized activities, recognized scenes,
sensor readings, context, or user-specified.
9. A system comprising: one or more computers programmed to perform
operations comprising: providing a visual representation of a
timeline of a video wherein the timeline comprises a plurality of
different time positions in the video; providing a visual
representation of one or more signals along the timeline of the
video, wherein each signal representation comprises a respective
intensity of the signal over the different time positions;
receiving, from each of a plurality of users, an indication of
interest in a portion of the video; and providing a visual
representation on the timeline of each indication of interest.
10. The system of claim 9, wherein the visual representation of the
signals comprises a visual representation of a weighted sum of
signals.
11. The system of claim 9, wherein a particular indication of
interest comprises a comment, a like, a share, or a highlight.
12. The system of claim 9, wherein the operations further comprise
determining a social signal based on the indications of interest,
wherein an intensity of the social signal over a length of the
video is based on a concentration of the indications of interest
over the length of the video.
13. The system of claim 12, wherein the operations further
comprise: receiving a second video comprising a plurality of
signals, each signal representing an identifiable type of content
over a length of the video; for at least one of the signals,
associating a weighting with the signal, wherein the weighting is
determined based at least in part on the social signal; and
identifying one or more portions of interest in the second video
based on the at least one signal weighting.
14. The system of claim 13, wherein the weighting is determined
based at least in part on the intensity of the social signal.
15. The system of claim 13, wherein the weighting is determined
based at least in part on indications of interest from a plurality
of videos.
16. The system of claim 9, wherein a particular signal represents
one of motion, sound, presence of faces, recognized faces,
recognized objects, recognized activities, recognized scenes,
sensor readings, context, or user-specified.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to and the benefit of U.S.
Provisional Patent Application No. 62/014,203, filed on Jun. 19,
2014; U.S. Provisional Patent Application No. 62/047,553, filed on
Sep. 8, 2014; and U.S. Provisional Patent Application No.
62/131,455, filed on Mar. 11, 2015, the entireties of which are
incorporated by reference herein.
BACKGROUND
[0002] The present disclosure relates generally to media curation,
editing and playback and, more particularly, to systems and methods
for identifying portions of interest in audio and video,
manipulating media segments using a simplified interface, and
forming a representative signature for audio and video based on
content signal intensity and social interest.
[0003] Creators of media content often generate substantially more
content than is needed or used in a final audio and/or video
production. Content creators may only be interested in showcasing
the most interesting or relevant portions of their generated
content to an audience. For example, a snowboarder may desire to
exhibit his best tricks on video, while discarding intermediate
portions of the video that show him boarding down a mountainside
between jumps. While the snowboarder can upload his video and
utilize complex video editing software to compile a highlights
video once he has returned from the slopes, identifying interesting
video segments, editing captured video, and sharing modified
content in the midst of his excursion is exceedingly difficult.
There is a need for systems and methods that facilitate the
foregoing tasks for content creators.
BRIEF SUMMARY
[0004] Systems and methods for video editing and playback are
disclosed herein. In one aspect, a computer-implemented method
comprises: providing a visual representation of a timeline of media
content wherein the timeline comprises a plurality of different
time positions in the media content; indicating a selected portion
of the timeline in the visual representation wherein the selected
portion is a continuous region of the timeline bounded by a first
border and a second border wherein each border corresponds to a
different respective time position on the timeline; receiving a
first user input action along a first axis of the timeline;
changing a position of the selected portion in the visual
representation along the timeline and based on the first user input
action; receiving a second user input action along a second axis of
the timeline; and resizing the selected portion in the visual
representation based on the second user input action wherein
resizing the selected portion comprises moving both of the
respective time positions of the borders to be closer to each other
or farther from each other. The media content can comprise at least
one of video and audio. Other embodiments of this aspect include
corresponding systems and computer programs.
[0005] In one implementation, the first axis is parallel to the
timeline. The first axis can also be coaxial with the timeline, and
the second axis can be perpendicular to the first axis. A
particular user input action can include a touchscreen gesture, a
mouse gesture, a tap, a click, a click-and-drag, a tracked
free-hand gesture, a tracked eye movement, a button press, or an
applied pressure.
[0006] In another implementation, the selected portion moves along
the timeline simultaneously with receiving the first user input
action. The first user input action can comprise a motion between a
first point on the timeline and a second point on the timeline, and
the selected portion can move from the first point to the second
point in direct correspondence with the first user input
action.
[0007] In one implementation, the second user input action
comprises a motion along a first direction of the second axis, and
the borders can be moved closer to each other simultaneously with
receiving the second user input action. The second user input
action can comprise a motion along a second direction, opposite the
first direction, of the second axis, and the borders can be moved
farther from each other simultaneously with receiving the second
user input action.
[0008] In a further implementation, the method comprises receiving
a third user input action along a third axis of the timeline; and
splitting the media content into a plurality of selected portions
based on a position of the selected portion on the timeline when
the third user input action is received. The third axis can be
perpendicular to the first axis and the second axis.
[0009] In yet another implementation, the timeline comprises visual
indicators identifying of portions of interest of the media
content.
[0010] In another aspect, a computer-implemented method comprises:
providing a visual representation of a timeline of media content
wherein the timeline comprises a plurality of different time
positions in the media content; indicating a selected portion of
the timeline in the visual representation wherein the selected
portion is a continuous region of the timeline bounded by a first
border and a second border wherein each border corresponds to a
different respective time position on the timeline; receiving a
user input action along an axis perpendicular to the timeline; and
resizing the selected portion in the visual representation based on
the user input action wherein resizing the selected portion
comprises moving both of the respective time positions of the
borders to be closer to each other or farther from each other.
Other embodiments of this aspect include corresponding systems and
computer programs.
[0011] In one implementation, a particular user input action is a
touchscreen gesture, a mouse gesture, a tap, a click, a
click-and-drag, a tracked free-hand gesture, a tracked eye
movement, a button press, or an applied pressure. The user input
action can comprise a motion along a first direction of the axis,
and the borders can be moved closer to each other simultaneously
with receiving the user input action. The user input action can
comprise a motion along a second direction, opposite the first
direction, of the axis, and the borders can be moved farther from
each other simultaneously with receiving the user input action.
[0012] In another implementation, the method further comprises
generating second media content based on the selected portion, the
second media content comprising at least a portion of the media
content.
[0013] In another aspect, a computer-implemented method comprises:
receiving a video comprising a plurality of signals, at least one
signal representing an identifiable type of content over a length
of the video; for at least one of the signals: identifying at least
one intermediate portion of interest in the video based on the
signal, and associating a weighting with the signal, wherein the
weighting is determined based at least in part on historical
attributes associated with at least one of an individual and a
group of users; and identifying one or more overall portions of
interest of the video based on the at least one intermediate
portion of interest and the at least one signal weighting. Other
embodiments of this aspect include corresponding systems and
computer programs.
[0014] In one implementation, the identifiable type of content for
a particular signal is selected from the group consisting of
motion, sound, presence of faces, recognized faces, recognized
objects, recognized activities, and recognized scenes. At least one
of the signals can comprise sensor readings over a length of the
video. The sensor can comprise an accelerometer, a gyroscope, a
heart rate sensor, a compass, a light sensor, a GPS, or a motion
sensor.
[0015] In another implementation, a particular intermediate portion
of interest in the video is identified based on an intensity of the
signal. Identifying a particular overall portion of interest of the
video can comprise: combining the signals according to the
respective weighting of each signals; identifying a portion of the
combined signals that meets a threshold signal intensity; and
identifying as the particular overall portion of interest a portion
of the media content that corresponds to the identified portion of
combined signals. Identifying a particular overall portion of
interest of the video can also comprise: combining the signals
according to the respective weighting of each signals; identifying
a portion of the combined signals that comprises a high or low
signal intensity relative to other portions of the combined
signals; and identifying as the particular overall portion of
interest a portion of the media content that corresponds to the
identified portion of combined signals.
[0016] In a further implementation, associating a weighting with a
particular signal comprises: training a classifier to predict
whether a given signal weighting would result in identifying a
portion of interest in media content using the historical
attributes associated with the group of users; and providing
attributes associated with the particular signal as input to the
classifier and obtaining the weighting for the particular signal as
output of the classifier.
[0017] In yet another implementation, the individual is an editor
of the video. A particular historical attribute associated with an
editor of the video can comprise: a propensity of the editor to
favor video content having a particular signal, a propensity of the
editor to favor video content lacking a particular signal, a
propensity of the editor to favor video content having a particular
signal with a particular signal intensity, a propensity of the
editor to disfavor video content having a particular signal, a
propensity of the editor to disfavor video content lacking a
particular signal, or a propensity of the editor to disfavor video
content having a particular signal with a particular signal
intensity.
[0018] A particular historical attribute associated with the group
of users can comprise: a propensity of the group of users to favor
video content having a particular signal, a propensity of the group
of users to favor video content lacking a particular signal, a
propensity of the group of users to favor video content having a
particular signal with a particular signal intensity, a propensity
of the group of users to disfavor video content having a particular
signal, a propensity of the group of users to disfavor video
content lacking a particular signal, or a propensity of the group
of users to disfavor video content having a particular signal with
a particular signal intensity.
[0019] In one implementation, the method further comprises: for at
least one of the signals, associating a second weighting with the
signal, wherein the second weighting is determined based at least
in part on historical attributes associated with one or more of an
editor of the video, a user other than the editor, and a group of
users; and identifying one or more second overall portions of
interest of the video based on the at least one intermediate
portion of interest and the at least one second signal
weighting.
[0020] In another implementation, the method further comprises:
providing a visual representation of a timeline of the video
wherein the timeline comprises a plurality of different time
positions in the video; and indicating the identified overall
portions of interest in the visual representation of the
timeline.
[0021] In another aspect, a computer-implemented method comprises:
providing a visual representation of a timeline of a video wherein
the timeline comprises a plurality of different time positions in
the video; providing a visual representation of one or more signals
along the timeline of the video, wherein each signal representation
comprises a respective intensity of the signal over the time
positions; receiving, from each of a plurality of users, an
indication of interest in a portion of the video; and providing a
visual representation on the timeline of each indication of
interest. Other embodiments of this aspect include corresponding
systems and computer programs.
[0022] In one implementation, the visual representation of the
signals comprises a visual representation of a weighted sum of
signals. A particular indication of interest can comprise a
comment, a like, a share, or a highlight.
[0023] In another implementation, the method further comprises
determining a social signal based on the indications of interest,
wherein an intensity of the social signal over a length of the
video is based on a concentration of the indications of interest
over the length of the video. The method can further comprise:
receiving a second video comprising a plurality of signals, each
signal representing an identifiable type of content over a length
of the video; for at least one of the signals, associating a
weighting with the signal, wherein the weighting is determined
based at least in part on the social signal; and identifying one or
more portions of interest in the second video based on the at least
one signal weighting. The weighting can be determined based at
least in part on the intensity of the social signal. The weighting
can also be determined based at least in part on indications of
interest from a plurality of videos.
[0024] In a further implementation, a particular signal represents
one of motion, sound, presence of faces, recognized faces,
recognized objects, recognized activities, recognized scenes,
sensor readings, context, or user-specified.
[0025] In another aspect, a computer-implemented method comprises:
receiving on a user device media content comprising a digital video
or a digital photograph; providing a plurality of filters that can
be applied to at least a portion of the media content; determining
a subset of the plurality of filters to recommend to a user of the
device based on one or more attributes associated with at least one
of the media content, the user, a group of users, or the user
device; visually identifying the subset of recommended filters;
receiving from the user a selection of one or more of the plurality
of filters; and applying the selected one or more filters to the
digital content.
[0026] A particular attribute associated with the media content can
include geolocation, a point of interest, motion, sound, a
recognized face, a recognized object, a recognized activity, or a
recognized scene. A particular attribute associated with the user
can include a historical filter preference of the user or a recent
filter preference of the user. A particular attribute associated
with a group of users can include a historical filter preference of
the group of users or a recent filter preference of the group of
users. A particular attribute associated with the user device can
include a property of an image sensor of the user device, a device
model, or an image capture setting.
[0027] In one implementation, determining the subset of the
plurality of filters to recommend to a user of the device
comprises: training a classifier to predict whether a given
combination of attributes would result in a particular filter being
selected by a user based on historical filter selections and
corresponding historical attributes associated with at least one of
media content, a user, a group of users, or a user device; and
providing one or more attributes associated with at least one the
media content, the user, a group of users, or the user device as
input to the classifier and obtaining the subset of recommended
filters as output of the classifier.
[0028] In one aspect, a computer-implemented method comprises:
providing a visual representation of a timeline of a video wherein
the timeline comprises a plurality of different time positions in
the video; indicating a selected portion of the timeline in the
visual representation wherein the selected portion is a continuous
region of the timeline bounded by a first border and a second
border wherein each border corresponds to a different respective
time position on the timeline; receiving a first user input action
along a first axis of the timeline; changing a position of the
first border along the timeline and based on the first user input
action; receiving a second user input action; and simultaneously
displaying a first portion of the video and a second portion of the
video. Other embodiments of this aspect include corresponding
systems and computer programs.
[0029] In one implementation, the method further comprises
receiving a third user input action along the first axis of the
timeline; and changing a position of the second border along the
timeline and based on the third user input action. The first axis
can be parallel to the timeline, as well as coaxial with the
timeline.
[0030] In another implementation, the second user input action
comprises a motion along a second axis of the timeline. The second
axis can be perpendicular to the first axis.
[0031] In a further implementation, the displayed first portion of
the video comprises an image corresponding to a portion of the
video at a beginning of the selected portion with respect to the
timeline. In another implementation, the displayed second portion
of the video comprises an image corresponding to a portion of the
video at an end of the selected portion with respect to the
timeline.
[0032] In yet another implementation, a particular user input
action is a touchscreen gesture, a mouse gesture, a tap, a click, a
click-and-drag, a tracked free-hand gesture, a tracked eye
movement, a button press, or an applied pressure.
[0033] In one implementation, the first border moves along the
timeline simultaneously with receiving the first user input action.
Likewise, the second border can move along the timeline
simultaneously with receiving the third user input action. The
first user input action can include a motion between a first point
on the timeline and a second point on the timeline, and the first
border can move from the first point to the second point in direct
correspondence with the first user input action. Similarly, the
third user input action can include a motion between a first point
on the timeline and a second point on the timeline, and the second
border can move from the first point to the second point in direct
correspondence with the third user input action.
[0034] In a further implementation, the method includes updating
the displayed first portion of the video to correspond to a change
in position of the first border along the timeline. The method can
further include updating the displayed second portion of the video
to correspond to a change in position of the second border along
the timeline
[0035] In another implementation, the video is a first video, and
the method further includes generating a second video based on the
selected portion, the second video comprising at least a portion of
the first video
[0036] The details of one or more implementations of the subject
matter described in the present specification are set forth in the
accompanying drawings and the description below. Other features,
aspects, and advantages of the subject matter will become apparent
from the description, the drawings, and the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0037] In the drawings, like reference characters generally refer
to the same parts throughout the different views. Also, the
drawings are not necessarily to scale, emphasis instead generally
being placed upon illustrating the principles of the
implementations. In the following description, various
implementations are described with reference to the following
drawings, in which:
[0038] FIG. 1 depicts an example system architecture for a video
editing and playback system according to an implementation.
[0039] FIG. 2 depicts an example user interface for a video editing
system according to an implementation.
[0040] FIGS. 3A and 3B depict example user input motions for
positioning a selected portion on a timeline.
[0041] FIGS. 4A and 4B depict example user input motions for
resizing a selected portion on a timeline.
[0042] FIG. 5 depicts an example user input motion for splitting
media content on a timeline.
[0043] FIG. 6 depicts a flowchart of an example method for
manipulating a selected portion of a timeline.
[0044] FIG. 7 depicts a flowchart of an example method for
weighting a plurality of signals.
[0045] FIG. 8 depicts an example user interface for a video
playback system according to an implementation.
[0046] FIG. 9 depicts a flowchart of an example method for
providing an experience map for media content.
[0047] FIG. 10 depicts a flowchart of an example method for
recommending a filter for application to media content.
[0048] FIGS. 11 and 12 depict example graphical user interfaces for
selecting a filter to apply to media content.
[0049] FIGS. 13A-13C depict example user input motions for defining
a selected portion on a timeline.
DETAILED DESCRIPTION
[0050] Described herein in various implementations are systems and
methods for editing, manipulating, and viewing media content. Media
content can include digital media encoded in a machine-readable
format, including but not limited to audio (e.g., sound recordings
of events, activities, performances, speech, music, etc.), video
(visual recordings of events, activities, performances, animation,
etc.), and other forms of media content usable in conjunction with
the techniques described herein. Media content can also include
streaming media (recorded or live).
[0051] FIG. 1 depicts an example high-level system architecture in
which an application 115 on a user device 110 communicates with one
or more remote servers 120 over communications network 150. The
user device 110 can be, for example, a smart phone, tablet
computer, smart watch, smart glasses, portable computer, mobile
telephone, laptop, palmtop, gaming device, music device,
television, smart or dumb terminal, network computer, personal
digital assistant, wireless device, information appliance,
workstation, minicomputer, mainframe computer, or other computing
device, that is operated as a general purpose computer or as a
special purpose hardware device that can execute the functionality
described herein.
[0052] The application 115 on the user device 110 can provide media
playback and editing functionality to a device user. In one
implementation, the application 115 provides a user interface that
allows a user to browse through, manipulate, edit, and/or play
media content (e.g., a video file, an audio file, etc.) using a
visual representation of a timeline. In another implementation, the
application 115 analyzes media content to identify one or more
portions of interest, which analysis can be based on a weighting of
various signals associated with the content. As used herein, a
"signal" refers to time-varying data describing an identifiable
type of content in audio, video, or other media content or a
portion thereof, including, but not limited to, motion data (e.g.,
displacement, direction, velocity, acceleration, orientation,
angular momentum, and time), sound, geographic location, presence
of faces, recognized faces, recognized objects, recognized
activities, and recognized scenes. A signal can also refer to a
time-varying or static attribute associated with media content or a
portion thereof, including, but not limited to, popularity (e.g.,
measurement of likes, recommendations, sharing), context, sensor
readings on a device (e.g., readings from an accelerometer,
gyroscope, heart rate sensor, compass, light sensor, motion sensor,
and the like), user label (e.g., a comment, hashtag, or other label
that can provide hints as to the content of a media file),
location, date, time, weather, and user-specified (e.g.,
manually-defined as interesting). Signal weighting data can be
stored locally on the user device 110 and/or can be transferred to
and received from remote server 120.
[0053] Remote server(s) 120 can aggregate signal weighting data,
social experience information, and other media analytics received
from user device 110 and other user devices 180 and share the data
among the devices over communications network 150. In some
implementations, remote server(s) 120 host and/or proxy media,
webpages, and/or other content are accessible by the user device
110 via application 115. Remote server(s) 120 can also perform
portions of the various processes described herein; for example,
analysis of media content to identify signals can be performed in
whole or in part remotely, rather than locally on the user device
110.
[0054] Third-party services 170 can include social networking,
media sharing, content distribution, and/or other platforms through
which a user can send, receive, share, annotate, edit, track, or
take other actions with respect to media content using, e.g.,
application 115 via communications network 150. Third-party
services 170 can include, but are not limited to, YouTube,
Facebook, WhatsApp, Vine, Snapchat, Instagram, Twitter, Flickr, and
Reddit.
[0055] Implementations of the present system can use appropriate
hardware or software; for example, the application 115 and other
software on user device 110 and/or remote server(s) 120 can execute
on a system capable of running an operating system such as the
Microsoft Windows.RTM. operating systems, the Apple OS X.RTM.
operating systems, the Apple iOS.RTM. platform, the Google
Android.TM. platform, the Linux.RTM. operating system and other
variants of UNIX.RTM. operating systems, and the like. The
software, can be implemented on a general purpose computing device
in the form of a computer including a processing unit, a system
memory, and a system bus that couples various system components
including the system memory to the processing unit.
[0056] Additionally or alternatively, some or all of the
functionality described herein can be performed remotely, in the
cloud, or via software-as-a-service. For example, as described
above, certain functions, such as those provided by the remote
server 120, can be performed on one or more servers or other
devices that communicate with user devices 110, 180. The remote
functionality can execute on server class computers that have
sufficient memory, data storage, and processing power and that run
a server class operating system (e.g., Oracle.RTM. Solaris.RTM.,
GNU/Linux.RTM., and the Microsoft.RTM. Windows.RTM. family of
operating systems).
[0057] The system can include a plurality of software processing
modules stored in a memory and executed on a processor. By way of
illustration, the program modules can be in the form of one or more
suitable programming languages, which are converted to machine
language or object code to allow the processor or processors to
execute the instructions. The software can be in the form of a
standalone application, implemented in a suitable programming
language or framework.
[0058] Method steps of the techniques described herein can be
performed by one or more programmable processors executing one or
more computer programs to perform functions by operating on input
data and generating output. Method steps can also be performed by,
and apparatus can be implemented as, special purpose logic
circuitry, e.g., an FPGA (field programmable gate array) or an ASIC
(application-specific integrated circuit). Modules can refer to
portions of the computer program and/or the processor/special
circuitry that implements that functionality.
[0059] Processors suitable for the execution of a computer program
include, by way of example, both general and special purpose
microprocessors. Generally, a processor receives instructions and
data from a read-only memory or a random access memory or both. The
essential elements of a computer are a processor for executing
instructions and one or more memory devices for storing
instructions and data. Information carriers suitable for embodying
computer program instructions and data include all forms of
non-volatile memory, including by way of example semiconductor
memory devices, e.g., EPROM, EEPROM, and flash memory devices;
magnetic disks, e.g., internal hard disks or removable disks;
magneto-optical disks; and CD-ROM and DVD-ROM disks. One or more
memories can store media assets (e.g., audio, video, graphics,
interface elements, and/or other media files), configuration files,
and/or instructions that, when executed by a processor, form the
modules, engines, and other components described herein and perform
the functionality associated with the components. The processor and
the memory can be supplemented by, or incorporated in special
purpose logic circuitry.
[0060] In some implementations, the user device 110 includes a web
browser, native application, or both, that facilitates execution of
the functionality described herein. A web browser allows the device
to request a web page or other program, applet, document, or
resource (e.g., from a remote server 120 or other server, such as a
web server) with an HTTP request. One example of a web page is a
data file that includes computer executable or interpretable
information, graphics, sound, text, and/or video, that can be
displayed, executed, played, processed, streamed, and/or stored and
that can contain links, or pointers, to other web pages. In one
implementation, a user of the user device 110 manually requests a
resource from a server. Alternatively, the device 110 automatically
makes requests with a browser application. Examples of commercially
available web browser software include Microsoft.RTM. Internet
Explorer.RTM., Mozilla.RTM. Firefox.RTM., and Apple.RTM.
Safari.RTM..
[0061] In other implementations, the user device 110 includes
client software, such as application 115. The client software
provides functionality to the device 110 that provides for the
implementation and execution of the features described herein. The
client software can be implemented in various forms, for example,
it can be in the form of a native application, web page, widget,
and/or Java, JavaScript, .Net, Silverlight, Flash, and/or other
applet or plug-in that is downloaded to the device and runs in
conjunction with a web browser. The client software and the web
browser can be part of a single client-server interface; for
example, the client software can be implemented as a plug-in to the
web browser or to another framework or operating system. Other
suitable client software architecture, including but not limited to
widget frameworks and applet technology can also be employed with
the client software.
[0062] A communications network 150 can connect user devices 110,
180 with one or more servers or devices, such as remote server 120.
The communication can take place over media such as standard
telephone lines, LAN or WAN links (e.g., T1, T3, 56kb, X.25),
broadband connections (ISDN, Frame Relay, ATM), wireless links
(802.11 (Wi-Fi), Bluetooth, GSM, CDMA, etc.), for example. Other
communication media are contemplated. The network 150 can carry
TCP/IP protocol communications, and HTTP/HTTPS requests made by a
web browser, and the connection between the client device and
servers can be communicated over such TCP/IP networks. Other
communication protocols are contemplated.
[0063] The system can also be practiced in distributed computing
environments where tasks are performed by remote processing devices
that are linked through a communications network. In a distributed
computing environment, program modules can be located in both local
and remote computer storage media including memory storage devices.
Other types of system hardware and software than that described
herein can also be used, depending on the capacity of the device
and the amount of required data processing capability. The system
can also be implemented on one or more virtual machines executing
virtualized operating systems such as those mentioned above, and
that operate on one or more computers having hardware such as that
described herein.
[0064] It should also be noted that implementations of the systems
and methods can be provided as one or more computer-readable
programs embodied on or in one or more articles of manufacture. The
program instructions can be encoded on an artificially-generated
propagated signal, e.g., a machine-generated electrical, optical,
or electromagnetic signal, that is generated to encode information
for transmission to suitable receiver apparatus for execution by a
data processing apparatus. A computer storage medium can be, or be
included in, a computer-readable storage device, a
computer-readable storage substrate, a random or serial access
memory array or device, or a combination of one or more of them.
Moreover, while a computer storage medium is not a propagated
signal, a computer storage medium can be a source or destination of
computer program instructions encoded in an artificially-generated
propagated signal. The computer storage medium can also be, or be
included in, one or more separate physical components or media
(e.g., multiple CDs, disks, or other storage devices).
[0065] FIG. 2 depicts an example user interface (UI) 200 of
application 115 for the playback and editing of media content, such
as audio and/or video captured by a mobile device. UI 200 includes
a visual timeline representation 220 that a user can manipulate to
navigate and/or edit the media content. In one implementation, if
the media content is streaming (whether live or prerecorded), the
timeline 220 can be dynamically updated or otherwise moving in a
synchronized manner with the media stream. Playback of the media
content, or a selected portion thereof, can be shown in display
window 210. If the media content does not include video or other
image-based content, the display window 210 can be hidden, blank,
or display a visual representation of the content (e.g., a sound
wave or captions for audio). UI 200 further includes a "Share"
button 240 that enables the user to indicate that the media content
can be transmitted from the user's device 110 to one or more
third-parties services 170. The application 115 can be configured
with the user's third-party service account information such that
the "Share" button 240 requires a single interaction to upload the
current media content to one or more of the services. The user can
also be provided with a dialog that allows the user to select which
third-party services will be sent the content. Communication
between the application 115 on the user device 110 and the
third-party services 170 can be direct, or in some implementations,
the application 115 provides the media content to remote server
120, which interfaces with the various third-party services 170 and
relays the content appropriately.
[0066] The timeline 220 can include graphical and/or textual
elements and can be, for example, a continuous track that includes
visual indicators (e.g., ticks, icons, colors, etc.) of different
time positions in the video. In one implementation, the timeline
220 includes thumbnails 225 of individual video frames representing
respective portions of a video file. The thumbnails 225 can hover,
change in size, scroll, and/or otherwise be manipulated on the
timeline 220 as the user interacts with the UI 200. In one example,
if the user device 110 includes a touchscreen interface (e.g., a
smartphone), the user can manipulate the video frame thumbnails 225
on the timeline 220 using his thumb, fingers, a stylus, or other
input apparatus. Based on the size of the thumbnails 225, screen
size, and/or display resolution of the device 110, only a portion
of the timeline 220 (and thus a subset of the thumbnails 225) may
be visible at any one time. The user then can move along the
timeline 220 by, for example, swiping the thumbnails 225 along an
axis of the timeline, moving the thumbnails 225 on or off the
visible portion of the timeline 220 on the device screen. In other
implementations, the entire timeline 220 and displayed thumbnails
225 are sized and/or positioned to fit on the device screen.
[0067] In one implementation, the timeline 220 includes one or
visual indicators delineating a selected portion 230 of the
timeline 220. The selected portion 230 can be a continuous region
of the timeline 220 that is bounded by a first border 234 and a
second border 236. Each border 234, 236 can correspond to a
different respective time position on the timeline 220. The
selected portion can be moved, resized, split, or otherwise
manipulated upon the application 115 receiving a user input action.
The user input action can be received via a component of the user
device 110 (e.g., touchscreen, touchpad, pointing stick, click
wheel, camera, microphone, gyroscope, accelerometer, built-in
keyboard, etc.) and/or an separate input peripheral coupled to the
user device 110 (e.g., mouse, keyboard, trackball, joystick,
camera, microphone, game controller, virtual headset, etc.). The
user input action can be, for example, a touchscreen gesture, a
mouse gesture, a tap, a click, a click-and-drag, a tracked eye
movement, a button press, an applied pressure, or other input
action suitable for allowing a user to manipulate the selected
portion 230 of the timeline 220. In one implementation, the user
input action is a tracked free-hand gesture captured in conjunction
with a user's use of an Oculus or other virtual reality device,
where the user is presented with the timeline 220 in
three-dimensional space, and the selected portion 230 where the
window can be adjusted based on a single free-hand movement along a
particular axis).
[0068] In various implementations, the selected portion 230 and/or
other components of the timeline 220 are manipulated by the one or
more user input actions. For example, the position of the selected
portion 230 on the timeline 220 can be changed by a user input
action substantially on or along a first axis of the timeline 220
(e.g., some deviation in the input action can be tolerated). The
first axis can be, e.g., parallel to or coaxial with the timeline
220, perpendicular to the timeline, angled, or disposed in another
position in relation to the timeline 220, in two-dimensional
coordinate space (x, y) or three-dimensional coordinate space (x,
y, z). There can be multiple axes that permit the user to change
the position of the selected portion 230. In some implementations,
the selected portion 230 follows the direction of the user's
finger, stylus, mouse cursor, or other means of input. In one
implementation, the user moves the selected portion 230 by
interacting directly with the selected portion 230 whereas, in
another implementation, the user can move the selected portion 230
by interacting with any portion of the timeline 220 and/or the
first axis. For instance, the user can cause the selected portion
230 to jump to a different area of the timeline 220 by tapping,
clicking, pressing, or taking other action with respect to the
different area. In further implementations, the first axis is
disposed above the timeline 220, below the timeline 220, or at
another position on the UI 200.
[0069] As shown in FIG. 3A, in one implementation, a user moves the
selected portion 230 (and simultaneously the corresponding borders
234 and 236) in direction 320 by swiping his thumb (or finger) 310
in the same direction 320 along horizontal (x) axis 330, which in
this example is coaxial with the timeline 220. Likewise, as shown
in FIG. 3B, the user can move the selected portion 230 (and
simultaneously the corresponding borders 234 and 236) in the
opposite direction 350 by swiping his thumb 310 in that opposite
direction 350 along horizontal axis 330. In some implementations,
the user's thumb 310 must be positioned on a border 234, 236 or
between the borders 234 and 236 to cause the selected portion 230
to move along the timeline 220. The repositioning of the selected
portion 230 can be simultaneous with and track the movement of the
user's thumb 310 (e.g., the further the user moves his thumb 310
along the horizontal axis 330, the further the selected portion 230
is moved on the timeline). Of note, by allowing the user to
reposition the selected portion 230 with a single thumb or finger,
the user is able to hold the user device 110 and operate the UI 200
with one hand.
[0070] In another example, the selected portion 230 can be resized
(i.e., one or both of the borders 234, 236 is moved closer or
further away from the other border(s)) by a user input action
substantially along a second, different axis of the timeline 220
(e.g., some deviation in the input action can be tolerated). The
second axis can be, e.g., parallel to or coaxial with the timeline
220, perpendicular to the timeline, angled, or disposed in another
position in relation to the timeline 220, in two-dimensional
coordinate space (x, y) or three-dimensional coordinate space (x,
y, z). There can be multiple axes that permit the user to resize
the selected portion 230. In some implementations, the selected
portion 230 is resized based on the direction of the user's finger,
stylus, mouse cursor, or other means of input. In one
implementation, the user resizes the selected portion 230 by
interacting directly with the selected portion 230 whereas, in
another implementation, the user can resize the selected portion
230 by interacting with any portion of the timeline 220 and/or the
second axis. In further implementations, the second axis is
disposed above the timeline 220, below the timeline 220, or at
another position on the UI 200.
[0071] In some implementations, the selected portion 230 can snap
to one or more various preset sizes while being resized. A
particular snap-to size can be based on which third-party
service(s) 170 are available to the user for sharing or saving
media content, which service(s) 170 the user has specifically
configured for use with the application 115, which service(s) 170
the user has previously used to share content, and/or which
services(s) 170 the system predicts the user will use to share
content. Custom snap-to sizes can also be manually configured by
the user. In some implementations, upon opening media content in
the application 115, the selected portion 230 defaults to a preset
size based on, for example, one or more of the above factors. As
one example, if the user most frequently posts video content to
Vine, the selected portion 230 can default to a six-second time
span.
[0072] As shown in FIG. 4A, in one implementation, a user resizes
(reduces the size of) the selected portion 230 (and simultaneously
moves the borders 234 and 236 inward toward each other; namely,
border 234 in direction 430 and border 236 in direction 440) by
swiping his thumb 310 in direction 420 along vertical (y) axis 410,
which in this example is perpendicular with the timeline 220 and
horizontal axis 330. Likewise, as shown in FIG. 4B, the user can
resize (increase the size of) the selected portion 230 (and
simultaneously move the borders 234 and 236 away from each other;
namely, border 234 in direction 460 and border 236 in direction
470) by swiping his thumb 310 in the opposite direction 450 along
vertical axis 410. The resizing of the selected portion 230 can be
simultaneous with and track the movement of the user's thumb 310
(e.g., the further the user moves his thumb 310 along the vertical
axis 410, the more the selected portion 230 is increased or reduced
in size). Of note, by allowing the user to resize the selected
portion 230 with a single thumb or finger, the user is able to hold
the user device 110 and operate the UI 200 with one hand.
[0073] In another example, a user can increase the size of the
selected portion 230 (i.e., move the borders 234 and 236 away each
other) by swiping his thumb 310 along vertical (y) axis 410 in a
direction away from the horizontal axis 330 (either up or down).
The user can then reduce the size of the selected portion 230 by
moving his thumb 310 in the opposite direction along vertical (y)
axis 410, back toward horizontal axis 330. Effectively, this is an
absolute value for the size of the selected portion 230 based on
distance up or down from horizontal axis 330.
[0074] In some implementations, minimum and/or maximum constraints
are set for the size of the selected portion 230. For example, if
the minimum length of a video is one second, the minimum length of
the selected portion 230 can also be set to one second. The maximum
selection length can also vary based on, e.g., where the video will
be shared (on Vine, the maximum length is six seconds).
[0075] In another example, the selected portion 230 can delineate a
portion of the media content that should be removed, retained, or
ignored (effectively splitting the media content) by a user input
action substantially along a third, different axis of the timeline
220 (e.g., some deviation in the input action can be tolerated).
The third axis can be, e.g., parallel to or coaxial with the
timeline 220, perpendicular to the timeline, angled, or disposed in
another position in relation to the timeline 220, in
two-dimensional coordinate space (x, y) or three-dimensional
coordinate space (x, y, z). There can be multiple axes that permit
the user to split the media content via the selected portion 230.
In one implementation, the user splits the media content by
interacting directly with the selected portion 230 whereas, in
another implementation, the user can split the media content by
interacting with any portion of the timeline 220 and/or the third
axis. In further implementations, the third axis is disposed above
the timeline 220, below the timeline 220, or at another position on
the UI 200.
[0076] As shown in FIG. 5, in one implementation, a user splits the
video by tapping, pressing, clicking (or other user input action)
along a z-axis perpendicular to both horizontal x-axis 330 and
vertical y-axis 410. For instance, by tapping on the selected
portion 230, the video is split into two portions 510a and 510b on
either side of the selected portion 230, and the video segment
delineated by the selected portion 230 is discarded from the video
or otherwise ignored for video playback, saving, and/or sharing. In
one implementation, by tapping on the selected portion 230, the
split portions 510a and 510b become selected portions, and the
selected portion 230 is deselected. In another example, by tapping
on the selected portion 230, the two portions 510a and 510b are
discarded from the video or otherwise ignored, leaving just the
selected portion 230. In some implementations, the user can create
multiple selected portions by interacting with multiple portions of
the timeline 220 (e.g., tapping on different areas of the timeline
220). The user can also interact again with a particular selected
portion 230 to invert or cancel the selection.
[0077] FIGS. 13A-13C illustrate an example technique for selecting
a starting point and ending point of a selected portion of a video
using a video editing interface. As shown in FIG. 13A, in one
implementation, a user can adjust the size of a selected portion
1330 of a video along a video timeline 1302 by manipulating the
left border 1334 of the portion 1330 independently of the right
border 1336. For example, the user can swipe his thumb (or finger)
1310 along horizontal axis 1380 of the video timeline 1302 to
increase (by swiping to the left) or decrease (by swiping to the
right) the size of the selected portion 1330. Display 1390 shows
one or more images derived from one or more frames of the video.
For example, display 1390 can show a static image or a video clip
(e.g., the portion of the video within the boundaries of the
selected portion 1330). The image(s) can correspond to the first
frame, last frame, or an intermediate frame(s) from a portion of
the video that is defined by the selected portion 1330.
[0078] FIG. 13B further illustrates the resizing of the selected
portion 1330 by manipulating the right border 1336 of the selected
portion 1330 independently of the left border 1334. As described
above with respect to the manipulation of the left border 1334, a
user can swipe his thumb (or finger) 1310 along horizontal axis
1380 of the video timeline 1302 to increase (by swiping to the
right) or decrease (by swiping to the left) the size of the
selected portion 1330. Again, display 1390 can show one or more
images from one or more frames (e.g., first, last, or intermediate
frame(s)) from a portion of the video defined by the selected
portion 1330.
[0079] Referring to FIG. 13C, the user can interact with the video
editing interface to create a split-display based on the selected
portion 1330. In one example, the interaction is a motion (e.g.,
swipe) that the user makes with his thumb (or finger) 1310 along a
different axis, such as vertical axis 1385. In other instances, the
interaction is a tap on the selected portion 1330, a tap on a
separate interface button, or other user action. In the depicted
example, the user swipes his thumb 1310 in a downward direction
along axis 1385, which causes display 1390 from FIGS. 13A and 13B
to split into two (or, in some instances, more than two) individual
displays 1390a, 1390b. In one implementation, display 1390a
corresponds to a portion of the video marked by or abutting border
1334 with respect to the timeline 1302, and display 1390b
corresponds to a portion of the video marked by or abutting border
1336 with respect to the timeline 1302. For example, display 1390a
can show an image or short video clip corresponding to the start of
the video within the selected portion 1330. Similarly, display
1390b can show an image or short video clip corresponding to the
end of the video within the selected portion 1330. Other displays
can show, e.g., intermediate portions of the video within the
selected portion 1330.
[0080] Referring back to FIG. 2, the timeline 220 can also include
visual indicators of portions of interest 250 in the media content,
as further described below. The visual indicators 250 can include
graphics and/or text, and can include various shapes, colors,
icons, animations, and so on, that indicate different types of
portions of interest (e.g., based on sound, motion, facial
recognition, and/or other signals). A particular visual indicator
250 can designate a starting point of a portion of interest, an
ending point of a portion of interest, and/or a continuous portion
of the content that is of interest. In some implementations, the
visual indicators facilitate a user's navigation of the timeline
220 by notifying (actively and/or passively) the user which
segments of the video may be interesting to the user. In some
implementations, the user can interact with a particular visual
indicator 250 to jump to that position in the timeline 220 and/or
to view more information about why the video segment is considered
to be interesting. In another implementation, the timeline 220
further includes visual indicators that identify points and/or
portions of interest manually tagged by a user, whether during or
after recording of the content.
[0081] FIG. 6 depicts one implementation of a method 600 for
manipulating the selected portion 230 in the timeline 220. In STEP
602, a visual representation of a timeline of media content is
provided in UI 200 of application 115. The timeline 220 comprises a
plurality of different time positions in the media content. A
selected portion 230 of the timeline 220 is indicated as a
continuous region of the timeline 220 bounded by a first border 234
and a second border 236 (STEP 606). Each border 234, 236 of the
region corresponds to a different respective time position on the
timeline 220. If the UI 200 receives a first user input action
along a first axis of the timeline 220 (e.g., a touchscreen swipe
along a horizontal timeline axis), the position of the selected
portion 230 is changed based on the first user input action (STEP
610). If the UI 200 receives a second user input action along a
second axis of the timeline 220 (e.g., a touchscreen swipe along a
vertical timeline axis), the selected portion 230 is resized based
on the second user input action (STEP 614). Resizing the selected
portion 230 can include moving both of the respective time
positions of the borders to be closer to each other or farther from
each other. If the UI 200 receives a third user input action along
a third axis of the timeline 220 (e.g., a tap along a z-axis of the
timeline 220), the media content is split into a plurality of
portions based on the position of the selected portion 230 on the
timeline 220 when the third user input action is received (STEP
618).
[0082] In one implementation, the application 115 on the user
device 110 automatically identifies one or more portions of media
content that may be of interest to the device user. The automatic
identification can also be performed remotely, wholly or in part,
by, e.g., remote server 120. Portions of interest in media content
can be automatically identified based on one or more signals
associated with the content. As described above, a signal can
represent an identifiable type of content within digital media
(e.g., motion, sound, recognized faces (known or unknown people),
recognized objects, recognized activities, recognized scenes, and
the like), as well as an attribute associated with the media (e.g.,
popularity, context, location, date, time, weather, news reports,
and so on).
[0083] A signal can vary in intensity over the length of an audio
file, video file, or other media content. "Signal intensity," as
used herein, refers to the presence of a particular content type in
media content and, in some cases, the extent to which the content
type exists in a particular portion of the media content. In the
case of explicit signals and certain attributes associated with
media content, signal intensity can be binary (e.g., exists or does
not exist). For content types such as motion, sound, facial
recognition, and so on, as well as certain sensor readings the
intensity can be a function of the concentration of the content
type in a particular portion of the media content, and can, for
example, vary over a fixed range or dynamic range (e.g., defined
relative to the intensities over the signal domain and/or relative
to other signals), or fall into defined levels or tiers (e.g., zero
intensity, low intensity, medium intensity, high intensity). In the
case of motion content, portions of a media file that are
determined to have higher instances of movement (or a particular
type of movement indicative of a particular activity such as, for
example, skiing or bicycle riding) will have correspondingly higher
motion intensity levels. As another example, the intensity of audio
content can be determined based on the loudness of audio in a
particular portion of media content. For general facial
recognition, intensity can be based on the number of identified
faces in a particular portion of a video (e.g., more faces equals
higher intensity). For known facial recognition, intensity can be
based on the number of identified faces that are known to a user in
a particular portion of a video (e.g., friends, family, social
networking connections, etc.). In the case of external sensor
readings associated with media content (e.g., an accelerometer in a
smartphone), intensity can be based on the amount strength of the
readings detected by the sensor (e.g., for the accelerometer,
stronger movement readings equals higher intensity).
[0084] Certain signals are considered "implicit," as they can be
automatically identified based on the media content or an
associated attribute. Implicit signals can include motion, sound,
facial/object recognition, popularity, context, and so on. Other
signals are "explicit," in that they can include manually defined
elements. For example, a user can manually tag a portion of a video
prior to, during, or after recording (e.g., via the UI 200) to
indicate that the portion should be considered interesting. In some
implementations, while recording audio and/or video, the user
manipulates a control (e.g., a button) on a recording device, on
the user device 110, or on another external device (e.g., a
wirelessly connected ring, wristwatch, pendant, or other wearable
device) in communication (e.g., via Bluetooth, Wi-Fi, etc.) with
the recording and/or user device 110, to indicate that an
interesting portion of the audio/video is beginning. The user can
then manipulate the same or a different control a second time to
indicate that the interesting portion has ended. The period between
the start and end time of the interesting portion can then be
considered as having a "user-tagged" signal.
[0085] FIG. 7 depicts one implementation of a method 700 for
identifying a portion of interest of media content. In STEP 702, a
video is received (e.g., at user device 110, remote server 120, or
other processing device). The video can include one or more
signals, such as those signals described above. For at least one of
the signals, an intermediate portion of interest in the video is
identified based on the respective signal (STEP 706). A particular
intermediate portion of interest of the video can be determined
based on the intensity of a signal associated with that portion.
For example, if a certain portion of the video has an incidence of
loud noise relative to the rest of the video, that certain portion
can be considered an intermediate portion of interest based on the
intensity of the audio signal. In some implementations,
intermediate portions of interest can be identified based on the
intensity of multiple signals within the respective portions.
[0086] In STEP 710, a weighting is associated with at least one of
the signals. For example, only motion and facial recognition might
be considered important for a particular video, so only those
signals are given a non-zero weighting. In another instance,
explicit signals are not included in the weighting. The weighting
can be personal to a particular user, general based on other users,
or a combination of both. More specifically, the weighting can be
determined based on historical attributes associated with a media
content editor (e.g., the user of the application 115, another
individual that is recognized for creating popular media content,
or other person or entity) and/or historical attributes associated
with a group of users (e.g., users who have created media content
with other application instances, users who have expressed interest
in media content created by the application user, and/or other
group of users whose actions can contribute to a determination of
the importance of a particular signal relative to other
signals).
[0087] For example, if a user creates skydiving videos and
frequently indicates that portions containing a high signal
intensity for sound are the most interesting to him (e.g., by
sharing videos that often contain such high-signal-intensity
portions), the system can allocate a higher weighting to the sound
signal relative to other signals (e.g., sound is weighted at 60%,
while the remainder of the signals make up the remaining 40%
weighting). This weighting can be applied to other videos edited by
the user and, in some instances, can be combined with weightings
based on the preferences of user groups, as indicated above. If
combined, individual and group weightings can be weighted equally
(e.g., as an initial default weighting), or in other instances, one
type can have a greater weight than the other. For example, if
there is little or no training data available for a particular
individual, the weightings based on user group preferences can be
weighted more heavily. In some implementations, signal weighting is
also dependent on the context or other attribute(s) associated with
particular media content. For instance, if the user prefers high
intensity sound signals in his skydiving videos, but prefers high
intensity motion signals in his snowboarding videos, the system can
weight the signals differently based on whether the user is editing
one type of video or the other.
[0088] Historical attributes of a content editor and/or group of
users can include the following: a propensity of the editor/group
to favor media content having a particular signal (e.g., sound is
preferred over motion, recognized faces, etc.), a propensity of the
editor/group to favor media content lacking a particular signal
(e.g., a video without recognized faces is preferred), a propensity
of the editor/group to favor media content having a particular
signal with a particular signal intensity (e.g., a high intensity
of motion is preferred in an action-oriented video), a propensity
of the editor/group to disfavor media content having a particular
signal (e.g., portions of a video which an ex-girlfriend's face
appears are disfavored), a propensity of the editor/group to
disfavor media content lacking a particular signal (e.g., video
without user-tagged portions is disfavored), and a propensity of
the editor/group to disfavor media content having a particular
signal with a particular signal intensity (e.g., portions of a
concert recording with a low intensity sound signal are
disfavored).
[0089] The system can refine the weightings it applies to
particular signals as data is collected over time relating to user
and group preferences of the signals and signal intensities. In
some implementations, the weighting process is facilitated or
automatically performed using machine learning, pattern
recognition, data mining, statistical correlation, support vector
machines, Gaussian mixture models, and/or other suitable known
techniques. In one example, signal attributes associated with
particular weightings can be viewed as vectors in a
multi-dimensional space, and the similarity between signal
attributes of unweighted signals and signals with particular
weightings (e.g., weightings that reflect preferred or otherwise
popular media portions by the user and/or other users) can be
determined based on a cosine angle between vectors or other
suitable method. If the similarity meets a threshold, an unweighted
signal can be assigned the weighting of the similar signal
vector.
[0090] As another example, a classifier (e.g., a suitable algorithm
that categorizes new observations) can be trained over time using
various historical data, such as the historical attributes referred
to above. A classifier can be personal to an individual user, and
use training data based only on that user's signal preferences and
other data. Other classifiers can be trained based on data
associated with the preferences of a group of users. For instance,
each time an editor shares media content or otherwise indicates
that a portion of the media content is of interest, the signal
information associated with the (portion of the) media content
(e.g. signal preference, signal intensity preference, etc.) can be
stored on the user device 110 and/or transferred to remote server
120 for use as training data to improve future weightings for the
editor and/or groups of users. The input to such a classifier
(e.g., upon creating new media content or opening a media file) can
include signal data, intensity data, media content attribute data,
and other information associated with the media content. The
classifier can then determine, based on the input and the training
data, an appropriate weighting of signals for the media
content.
[0091] Still referring to FIG. 7, in STEP 714, one or more overall
portions of interest of the media content are identified based on
the intermediate portion(s) of interest and the signal
weighting(s). An overall portion of interest can be identified by
combining the signals according to their respective weightings, and
selecting a portion of the combined signals (corresponding to a
portion of the media content) that meets a threshold signal
intensity. Alternatively or in addition, the top or bottom N
combined signal intensity points (e.g., top/bottom one, top/bottom
three, top/bottom five, top/bottom ten, etc.) can be used to
determine the overall points of interest. For example, the top
three points (in non-overlapping regions) can be identified, and
the segments of the media content that surround each point (e.g.,
+/- N seconds on either side) can be considered overall portions of
interest. To illustrate, when a user creates or opens a video via
the application 115, the application 115 can suggest one or more
portions of the video that might be of interest to the user (e.g.,
by a suitable form of visual indication), based on signals in the
video and weightings determined based on the user, another user,
and/or groups of users. In one implementation, the application 115
presents different signal weightings (and, in some cases, the
corresponding portions of interest) to the user (e.g., a weighting
based on the user's preferences, a weighting based on an expert's
preferences, and/or a weighting based on a group of users'
preferences) and allows the user to select which weighting(s)
and/or portions of interest the user prefers.
[0092] FIG. 8 depicts one implementation of a video playback
interface 800 (which can be included in application 115) through
which users can play media content (e.g., audio, video, etc.)
created and/or edited with the application 115 or by other means.
Interface 800 includes a visual representation of a timeline 810,
which defines different time positions in the media content and can
be manipulated by a user to navigate through media content shown in
display window 820.
[0093] Interface 800 also includes visual representations of
signals 830 associated with the media content. The signal
representations 830 can be disposed on, above, below, or otherwise
proximate to the timeline, and can depict the intensity of one or
more of the media content signals as a waveform over the length of
the media content. If multiple signals waveforms are displayed,
each can be a different color and/or include some other
differentiating identifier (e.g., identifying text displayed upon
selection or hover). In some implementations, the user can
configure the interface 800 to display all signals associated with
the media content, no signals, or a subset thereof (e.g., the user
can toggle the display of individual signals, implicit signals,
explicit signals, and other signal categories). The user can also
configure the interface 800 to display the signals separately,
display the sum of the signals, and/or display various weighted
sums of signals (e.g., a user-based weighting, a group-based
weighting, etc.).
[0094] Users can express their interest in particular points or
portions of media content by "liking" that point or portion while
the content is playing in display window 820. In other
implementations, users can navigate to a particular portion of the
media content using the timeline and "like" the point or portion
whether the media is playing, paused, or stopped. A user can
actively "like" the media content using, e.g., a button on the
interface 800, and/or by other suitable user input actions (e.g.,
touchscreen gesture, mouse gesture, tracked free-hand gesture,
etc.) applied or directed to the display window 820, timeline 810,
or other portion of the interface 800. The "like" can be visually
represented on the timeline 810 to the user and/or other users
viewing the same media content using a suitable graphical and/or
textual indicator (e.g., colored shape, icon, pop-up text,
combinations thereof, and so on). For example, in the depicted
implementation, visual like indicators 840 are disposed at each
location on the timeline where a user "liked" the corresponding
media content.
[0095] Similarly, users can comment on a point or portion of the
media content using, e.g., a button on the interface 800 that opens
a text box, and/or other suitable user interface control. The user
comments can be visually represented on the timeline 810 to the
user and/or other users viewing the same media content. For
example, in the depicted implementation, visual comment indicators
850 are disposed at each location on the timeline where a user
commented on the corresponding media content. The visual comment
indicators 850 can include a thumbnail of the user's avatar or
profile image (e.g., corresponding to a social networking or other
account), and/or can include other suitable graphical and/or
textual indicators.
[0096] Users can also indicate their interest in the media content
or portions thereof in various other manners. For example, a user
can select a portion of the media content to share with others
through a social network, such as Facebook. A user can also
highlight or provide some other visual identification of a portion
of interest through the interface 800, and the identification can
then be made available to other users, individually or in
combination with other indications of interest. In one
implementation, a heat map can be used to identify varying levels
of interest in portions of the media content.
[0097] The presence of likes, comments, recommendations, and/or
other indications of social interest over the length of media
content can constitute a social signal, which has an intensity that
varies in relation to popularity. For example, portions of media
content that have a higher concentration of likes, comments, and
other indicators of interest relative to other portions of the
media content will have a higher social signal intensity. In some
implementations, the social signal can be used in further refining
signal weightings for the corresponding media content or other
media content. In one example, if a video is published in which the
motion signal was heavily weighted compared to other signals, but
others users, upon viewing the video, prefer portions of the video
in which many faces appear (i.e., the social signal has a higher
intensity at these face portions), then the social signal may cause
future weightings of the media content or other media content to be
biased more toward the facial recognition signal. More
specifically, social signals can become part of the training data
that influences the determination of signal weights.
[0098] Ultimately, the interface 800 provides for an overall
"signature" that reflects the experience associated with a video
(or other form of media content) and that can be quickly
apprehended by a user accessing the particular video. The signature
can include a visual representation of the implicit and/or explicit
signals associated with the video (e.g., separate signals, a
weighted combination), such as sound, motion, faces, and the social
signal. Thus, from implicit signals, social signals, user labels,
and other information associated with the video, a user can easily
determine what types of content the video includes as well as what
portions are most popular to other users.
[0099] In some implementations, multiple videos can be represented
that are cotemporaneous on the timeline, for instance, as spatially
stacked thumbnails contiguous in the y-axis and/or with edges
stacked like a stack of papers to provide either a full
representation of multiple points of view of the same event
spatially displayed, or the foremost video stream with other points
of view selectable by the user. The signature can represent the
foremost video or the aggregate signature of all or a subset of the
cotemporaneous videos. The set of potential cotemporaneous signals
can include large sets of video points of view to represent a
single event, such as a baseball game, or a class event, such as
all snowboarding on planet Earth at this moment. The same stacking
in a different axis (such as the x-axis) can be used to represent
jumps in time from the same point of view. For instance, two
contiguous pieces of video, two years apart from the same video
source such as a home video camera, can be displayed one after the
other. The stacking on multiple axes, either spatially contiguous
or occluding, can be combined to represent a large number of points
of view across a large amount of time, such as all wearable video
cameras ever used at a football stadium. The resulting signature
can represent either part or all of the videos.
[0100] FIG. 9 depicts an example method 900 for providing an
experience map of media content. In STEP 902, a visual
representation of a timeline of a video is provided (e.g., by
application 115 via interface 800). The timeline includes a
plurality of different time positions in the video. Visual
representations of one or more signals along the timeline of the
video are also provided, as described above (STEP 906). Each signal
representation includes an intensity of an identifiable type of
content over the time positions. Identifiable types of content can
include, for example, motion, sound, presence of faces, recognized
faces, recognized objects, recognized activities, and recognized
scenes.
[0101] From each of a number of users, an indication of interest in
a portion of the video is received via interface 800 (STEP 910),
and each indication of interest is visually represented on the
timeline (STEP 914). The indications of interest can be, for
example, comments, likes, share, highlights, and the like, and can
be graphically and/or textually represented by the interface 800 as
described above.
[0102] In some implementations, a social signal is determined based
on the indications of interest. The intensity of the social signal
over the length of the video can be based on the concentration of
the indications of interest over the video length (e.g., more likes
for a particular portion relative to other video portions equals a
higher intensity social signal for that particular portion). As
described above, the social signal can be incorporated into the
training data of the system where it can influence determined
signal weightings for various media content, in conjunction with
social signals for other media content and other historical
attributes associated with users and groups of users.
[0103] In one implementation, signals and/or other attributes can
be used to recommend appropriate filters for application
(automatically or by a user) to media content. FIG. 10 illustrates
an example method 1000 for providing filter recommendations. In
STEP 1002, media content is received on the user device 110. For
example, a digital photograph or video can be captured using a
camera on the device 110, media content can be downloaded onto the
device 110, and so on. The application 115 on the device 110 can
provide an interface that allows the user to edit the media content
by applying one or more digital filters to the content. A "filter"
refers to a modifier of an image or other media content. Filters
can provide color enhancement and conversion, changes to brightness
and contrast, polarization, diffusion, special effects, and other
changes to media content. The application 115 can provide filter
functionality based on filters stored on the device 110, custom
filters created by the user or other users, and/or filters defined
by or retrieved from other sources, such as Instagram. The various
available filters can be visually provided to the user for
selection via a user interface such as that shown in FIGS. 11 and
12 (STEP 1006).
[0104] In STEP 1010, the application 115 determines a subset of the
available filters (e.g., 4 to 8 filters) to recommend to the user
based on one or more attributes. The attributes can relate to the
media content (the entire content or a portion thereof), the user,
and/or the mobile device. For example, attributes associated with
the media content can include geolocation, location, point of
interests, and a signal in the media content (e.g., motion, sound,
recognized faces (known or unknown people), recognized objects,
recognized activities, and recognized scenes). Attributes can be
derived from metadata associated with the media content or by
processing the content (e.g., audio, video, imagery) itself. For
instance, to determine the geolocation of the content capturing
device (e.g., mobile phone), or the location or nearby points of
interest with respect to where the media content was captured,
geotag information in the content can be examined. Further, a
database or other data structure tracking points of interest (e.g.,
parks, museums, buildings, geographic features, etc.) can be
correlated with the location information to identify which points
of interest are located at or nearby the location associated with
the media content. Points of interest can be matched directly or
semantics in the name of the point of interest (e.g. the word
"beach") can be used. In addition, images and video can be
processed to recognize particular activities (e.g., snowboarding,
skydiving, driving, etc.), particular objects or scenes (e.g., the
Empire State Building, the Boston skyline, a snow-covered
mountain), weather, lighting conditions, and so on, and audio can
be processed to further inform the recognition process (e.g., the
sounds of a beach, a crowd, music, etc., can be processed to help
identify a location or event).
[0105] Attributes used to inform the recommendation of the filters
can also include attributes associated with the device that
captured the media content (e.g., the user device 110), such as
device model, camera type, image sensor resolution, use of flash,
white balance, exposure, other camera settings, and so on. This
information can be derived, for example, from metadata included in
the media content or by examining the capturing device settings and
properties during or after capturing media content. Other
attributes can include information associated with the device user,
the user who captured the media content, or a group of users. For
example, the previous behavior and/or preferences of a particular
user or a group of users with respect to filter selection can
influence what filters are recommended by the application 115. If,
for instance, the user, the user's followers, all users, or another
identified group select a sepia tone filter for portraits 50% of
the time, then that filter can be highly recommended the next time
the user captures a portrait-type photograph.
[0106] The various machine learning techniques described herein can
applied to filter selection. For instance, a classifier can be
trained over time using as input attributes (such as those
described above) for particular media content in combination with
the filter or filters that were selected and applied to the media
content. Thus, as an example, the classifier might learn over time
that filters that reduce glare and enhance blue tones are often
applied to media content that include video or images of activities
taking place on snow, such as skiing or snowboarding. As another
example, the classifier might learn over time that filters that add
warmth and reduce contrast are frequently selected for media
content captured near Venice Beach. Accordingly, to determine a
recommendation of filters that are likely to be selected by a user
from all available filters, one or more attributes can be input to
the classifier and the recommendation can be obtained as output.
The recommendation can include one or more filters ranked in order
of likelihood of selection by the user.
[0107] In STEP 1014, the application 115 visually identifies the
recommended filters. For example, the application can provide a
display of the media content or a portion thereof with a
recommended filter applied, accompanied by the name of the filter.
In the case of a video, the display can include multiple copies of
an image frame from the video arranged on the device screen, each
with a different recommended filter applied as a preview (see FIG.
11). The user can then select the desired filter to apply by
interacting with the display (e.g., via touch). The user can also
manipulate the media content to preview applied filters to other
portions of the media content, and then apply one or more filters
(recommended or not recommended) to all or a portion of the media
content. Accordingly, in STEP 1018, the filter selection is
received by the application 115, which applies the selected
filter(s) to the designated media content or portion of the media
content (STEP 1022).
[0108] The terms and expressions employed herein are used as terms
and expressions of description and not of limitation, and there is
no intention, in the use of such terms and expressions, of
excluding any equivalents of the features shown and described or
portions thereof. In addition, having described certain
implementations in the present disclosure, it will be apparent to
those of ordinary skill in the art that other implementations
incorporating the concepts disclosed herein can be used without
departing from the spirit and scope of the invention. The features
and functions of the various implementations can be arranged in
various combinations and permutations, and all are considered to be
within the scope of the disclosed invention. Accordingly, the
described implementations are to be considered in all respects as
illustrative and not restrictive. The configurations, materials,
and dimensions described herein are also intended as illustrative
and in no way limiting. Similarly, although physical explanations
have been provided for explanatory purposes, there is no intent to
be bound by any particular theory or mechanism, or to limit the
claims in accordance therewith.
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