U.S. patent application number 17/525413 was filed with the patent office on 2022-05-05 for filtering detected objects from an object recognition index according to extracted features.
This patent application is currently assigned to Amazon Technologies, Inc.. The applicant listed for this patent is Amazon Technologies, Inc.. Invention is credited to Vivek Bhadauria, Binglei Du, Sean R. Flynn, Jonathan Hedley, Keith Young Johnson, Vasant Manohar, Kunwar Yashraj Singh, Dylan C. Thomas, Wei Xia.
Application Number | 20220139063 17/525413 |
Document ID | / |
Family ID | 1000006093940 |
Filed Date | 2022-05-05 |
United States Patent
Application |
20220139063 |
Kind Code |
A1 |
Singh; Kunwar Yashraj ; et
al. |
May 5, 2022 |
FILTERING DETECTED OBJECTS FROM AN OBJECT RECOGNITION INDEX
ACCORDING TO EXTRACTED FEATURES
Abstract
Objects detected in data may be filtered from an object
recognition index. Data for object detection may be received. An
object detection technique may be applied to the data to detect an
object. If the object does not satisfy indexing criteria for the
object recognition index, then the detected object may be excluded
from the object recognition index.
Inventors: |
Singh; Kunwar Yashraj;
(Bellevue, WA) ; Johnson; Keith Young; (Seattle,
WA) ; Bhadauria; Vivek; (Redmond, WA) ; Flynn;
Sean R.; (Boise, ID) ; Du; Binglei; (Seattle,
WA) ; Thomas; Dylan C.; (Redmond, WA) ;
Manohar; Vasant; (Bothell, WA) ; Hedley;
Jonathan; (Seattle, WA) ; Xia; Wei; (Seattle,
WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Amazon Technologies, Inc. |
Seattle |
WA |
US |
|
|
Assignee: |
Amazon Technologies, Inc.
Seattle
WA
|
Family ID: |
1000006093940 |
Appl. No.: |
17/525413 |
Filed: |
November 12, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16183365 |
Nov 7, 2018 |
11176403 |
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17525413 |
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62727983 |
Sep 6, 2018 |
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Current U.S.
Class: |
382/103 |
Current CPC
Class: |
G06V 10/40 20220101;
G06K 9/6262 20130101; G06V 40/16 20220101 |
International
Class: |
G06V 10/40 20060101
G06V010/40; G06K 9/62 20060101 G06K009/62; G06V 40/16 20060101
G06V040/16 |
Claims
1. A system, comprising: at least one processor; and a memory,
storing program instructions that when executed by the at least one
processor cause the at least one processor to: receive data to add
objects detected in the data to an object recognition index; detect
an object in the data according to an object detection technique;
extract one or more features for the detected object; evaluate the
one or more features of the detected object according to one or
more indexing criteria to determine whether to exclude objects from
the object recognition index; and responsive to a determination
that the detected object does not satisfy the one or more indexing
techniques, exclude the detected object detected from the object
recognition index according to a determination that the one or more
features of the object do not satisfy the one or more indexing
criteria.
2. The system of claim 1, wherein the program instructions further
cause the at least one processor to return the representation of
the one or more features of the detected object responsive to a
request for objects not included in the object recognition
index.
3. The system of claim 1, wherein to extract the one or more
features for the detected object, the program instructions cause
the at least one processor to: apply a convolutional neural network
trained to indicate similarity between detected objects to the
detected object in order to determine a feature vector that
represents the one or more features of the detected object; or
apply the convolutional neural network to determine one or more
intermediate features in order to determine the one or more
features as domain specific attributes of the detected object.
4. The system of claim 1, wherein the at least one processor and
the memory are implemented as part of an object recognition service
of a provider network that detects human faces in images, wherein
the data is image data, wherein the object recognition index is
hosted in the provider network, and wherein the detection,
evaluation, extraction, and exclusion are performed responsive to a
request to index the image data as part of the object recognition
index.
5. A method, comprising: applying, by one or more computing
devices, an object detection technique on data to detect an object
within the data to be considered for inclusion in an object
recognition index; evaluating, by the one or more computing
devices, one or more features of the detected object determined as
part of the application of the object detection technique with
respect to one or more indexing criteria to exclude objects from
the object recognition index that do not satisfy the one or more
indexing criteria; and excluding, by the one or more computing
devices, the detected object detected from the object recognition
index according to a determination that the one or more features of
the object do not satisfy the one or more indexing criteria.
6. The method of claim 5, further comprising receiving, by the one
or more computing devices, a request to index objects within the
data, the applying, the evaluating, and the excluding are performed
responsive to the request.
7. The method of claim 6, further comprising: responsive to the
request, obtaining the data from a data store indicated by the
request.
8. The method of claim 6, wherein the request includes a parameter
that requests application of the one or more indexing criteria when
considering objects detected in the data for inclusion in the
object recognition index.
9. The method of claim 5, further comprising: wherein the applying
of the object detection technique on the data detects a second
object within the data; evaluating, by the one or more computing
devices, one or more features of the second detected object
determined as part of the application of the object detection
technique with respect to the one or more indexing criteria; and
including, by the one or more computing devices, the second
detected object in the object recognition index according to a
determination that the one or more features of the second detected
object satisfy the one or more indexing criteria.
10. The method of claim 5, further comprising: after an update to
the object detection technique: obtaining, by the one or more
computing devices, respective features for true and falsely
detected objects according to the updated object detection
technique applied to a plurality of data; training, by the one or
more computing devices, a predictive model according to the
obtained features to identify respective feature values that
maximize a prediction of one of the true detected objects upon
application of the predictive model to the respective features of
the one true detected object; and updating, by the one or more
computing devices, the one or more indexing criteria based on the
respective features identified by the predictive model that
maximize the prediction of true detected objects.
11. The method of claim 5, further comprising returning, by the one
or more computing devices, a response via an interface indicating
that the object was not included in the object recognition index
and an indication of at least one of the respective features of the
object that failed to satisfy the one or more indexing
criteria.
12. The method of claim 5, wherein the object detection technique
is a natural language processing technique.
13. The method of claim 5, further comprising: applying, by the one
or more computing devices, a second object detection technique on
second data to detect an object within the second data to be
considered for inclusion in an second object recognition index;
evaluating, by the one or more computing devices, one or more
features of the detected object determined as part of the
application of the second object detection technique with respect
to one or more other indexing criteria to exclude objects from the
second object recognition index that do not satisfy the one or more
indexing criteria, wherein the one or more other indexing criteria
are different than the one or more indexing criteria; and
excluding, by the one or more computing devices, the detected
object detected from the second object recognition index according
to a determination that the one or more features of the object do
not satisfy the one or more other indexing criteria.
14. One or more non-transitory, computer-readable storage media,
storing program instructions that when executed on or across one or
more computing devices cause the one or more computing devices to
implement: receiving image data to add objects detected in the
image data to an object recognition index; applying an object
detection technique on the image data to detect an object within
the image data; evaluating one or more features of the detected
object determined as part of the application of the object
detection technique with respect to one or more indexing criteria
to exclude objects from the object recognition index that do not
satisfy the one or more indexing criteria; and excluding the
detected object detected from the object recognition index
according to a determination that the one or more features of the
object do not satisfy the one or more indexing criteria.
15. The one or more non-transitory, computer-readable storage media
of claim 14, wherein the program instructions cause the one or more
computing devices to further implement: wherein the applying of the
object detection technique on the image data detects a second
object within the image data; evaluating, by the one or more
computing devices, one or more features of the second detected
object determined as part of the application of the object
detection technique with respect to the one or more indexing
criteria; and including, by the one or more computing devices, the
second detected object in the object recognition index according to
a determination that the one or more features of the second
detected object satisfy the one or more indexing criteria.
16. The one or more non-transitory, computer-readable storage media
of claim 14, wherein the applying of the object detection technique
on the image data includes application of a convolutional neural
network trained to indicate similarity between detected objects to
the detected object in order to determine a feature vector that
represents the one or more features of the detected object
17. The one or more non-transitory, computer-readable storage media
of claim 14, wherein the program instructions cause the one or more
computing devices to further implement: after an addition of a
second object detection technique that can be performed by the one
or more computing devices: obtaining, respective features for true
and falsely detected objects according to the added object
detection technique applied to a plurality of image data; training
a predictive model according to the obtained features to identify
respective feature values that maximize a prediction of one of the
true detected objects upon application of the predictive model to
the respective features of the one true detected object; and adding
one or more indexing criteria for the added object detection
technique based on the respective features identified by the
predictive model that maximize the prediction of true detected
objects.
18. The one or more non-transitory, computer-readable storage media
of claim 14, wherein the program instructions cause the one or more
computing devices to further implement: receiving a request to
index a second image data for inclusion in the object recognition
index, wherein the request includes a parameter that disables
application of the one or more indexing criteria when considering
objects detected in the image data for inclusion in the object
recognition index; applying the object detection technique on the
second image data to detect an object within the second image data;
and including the object detected in the second image data in the
object recognition index.
19. The one or more non-transitory, computer-readable storage media
of claim 14, wherein the program instructions cause the one or more
computing devices to further implement: storing a representation of
the one or more features of the detected object; and returning the
representation of the one or more features of the detected object
responsive to a request for objects not included in the object
recognition index.
20. The one or more non-transitory, computer-readable storage media
of claim 14, wherein the one or more computing devices are
implemented as part of an object recognition service of a provider
network, wherein the object recognition index is hosted in the
provider network, and wherein the detection, evaluation,
extraction, and exclusion are performed responsive to a request to
index the image data as part of the object recognition index.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 16/183,365, filed Nov. 7, 2018, which claims
benefit of priority to U.S. Provisional Application Ser. No.
62/727,983, filed Sep. 6, 2018, which are incorporated herein by
reference in their entirety.
BACKGROUND
[0002] Computer vision or other object recognition techniques
offers computers many capabilities to performance various tasks
that might otherwise be impossible for the computer to perform in
different scenarios. Object recognition has, for instance, many
different applications to facilitate diverse technologies and
systems, including automated vehicle operation, assisted medical
operations, or identity services to provide secure payment or other
transactions. In order to facilitate object recognition, techniques
may be implemented to manage and evaluate the large amount of image
data that can be captured as part of object recognition. Techniques
that improve the selection and evaluation of image data in such
large scale settings are thus highly desirable.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 illustrates a logical diagram of filtering detected
objects from an object recognition index according to extracted
features, according to some embodiments.
[0004] FIG. 2 illustrates an example provider network that may
implement a service that implements an object recognition service
that filters detected objects from an object recognition index
according to extracted features, according to some embodiments.
[0005] FIG. 3 illustrates a logical block diagram for indexing
objects from data, according to some embodiments.
[0006] FIG. 4 illustrates an example interface for displaying
indexing results, according to some embodiments.
[0007] FIG. 5 illustrates a logical block diagram of object
recognition using an object recognition index, according to some
embodiments.
[0008] FIG. 6 illustrates a high-level flowchart of various methods
and techniques to implement filtering detected objects from an
object recognition index according to extracted features, according
to some embodiments.
[0009] FIG. 7 illustrates a high-level flowchart of various methods
and techniques to implement determining indexing criteria to filter
detected objects, according to some embodiments.
[0010] FIG. 8 illustrates an example system to implement the
various methods, techniques, and systems described herein,
according to some embodiments.
[0011] While embodiments are described herein by way of example for
several embodiments and illustrative drawings, those skilled in the
art will recognize that embodiments are not limited to the
embodiments or drawings described. It should be understood, that
the drawings and detailed description thereto are not intended to
limit embodiments to the particular form disclosed, but on the
contrary, the intention is to cover all modifications, equivalents
and alternatives falling within the spirit and scope as described
by the appended claims. The headings used herein are for
organizational purposes only and are not meant to be used to limit
the scope of the description or the claims. As used throughout this
application, the word "may" is used in a permissive sense (i.e.,
meaning having the potential to), rather than the mandatory sense
(i.e., meaning must). Similarly, the words "include," "including,"
and "includes" mean including, but not limited to.
[0012] It will also be understood that, although the terms first,
second, etc. may be used herein to describe various elements, these
elements should not be limited by these terms. These terms are only
used to distinguish one element from another. For example, a first
contact could be termed a second contact, and, similarly, a second
contact could be termed a first contact, without departing from the
scope of the present invention. The first contact and the second
contact are both contacts, but they are not the same contact.
DETAILED DESCRIPTION OF EMBODIMENTS
[0013] Various embodiments of filtering detected objects from an
object recognition index according to extracted features are
described herein. Object detection techniques, like those that
utilize deep neural networks, have achieved state of the art
performance in computer vision tasks such as object recognition
(e.g., classifying image objects, recognizing human faces,
detecting text in image data, comparing or identifying similar or
matching objects, natural language processing, etc.). In various
embodiments, the discriminative features extracted by these
techniques (e.g., features extracted by deep neural networks and/or
features determined as attributes based on these features) may
allow systems to identify similar objects by extracting features of
a query image and running a similarity search over a collection of
stored feature vectors. The ability to search for similar objects
over a large collection of stored object features may allow for
multiple applications. For example, if the detected object is a
human face and the searched objects are human faces, then systems
can provide face recognition, face search, and person
re-identification, in some embodiments.
[0014] The quality of features extracted by the object detection
technique may determine the accuracy of the search, in some
scenarios. If, for instance, the extracted features are not
discriminative enough, a similarity search over objects stored in
an index according to those features may degrade in performance or
accuracy (e.g., resulting in increased false matches). The quality
of features extracted by object detection technique may, in some
embodiments, be impacted by false positive object detections (e.g.,
detecting non-face images as a face) and/or low-quality object
detections (e.g., such as a blurry or out-of-focus faces detected
in an image). This impact may be compounded over time as a
collection of detected objects in an object recognition index
increases in size because the presence of such false positive or
low-quality object detections may increase as well, which could
cause the search quality to decrease as searches may consider the
false positive or low quality objects when making a search. For
example, in a face recognition scenario, a sharp face in a query
image might get matched to a low-quality blurry image of a
different person or an object that is not actually a human face,
which would reduce the accuracy of the face-search and make it
difficult for a user to understand the result or for a system to
accurately perform some function (e.g., face identification for
security systems that allow users into a building).
[0015] In various embodiments, filtering detected objects for
inclusion in an object recognition index may be performed to
substantially reduce (or exclude entirely) low-quality or false
positive object detections using features of the image data
extracted when the objects were detected. Examples of such features
may be general across many different types of objects that can be
detected, such as brightness, sharpness, confidence, and bounding
box in image data, or specific to a particular type of object being
detected (e.g., face pose features for face detection or sentiment
for natural language processing). Indexing criteria that includes
combinations of one or more feature values (or range of feature
values) that indicate which detected objects should be included
(and which should be excluded) from an object recognition index,
for instance may be applied to filter detected objects. In at least
some embodiments, a weighted combination of the features may be
used to filter detect objects.
[0016] In various embodiments, filtering detected objects for
inclusion in an object recognition index may significantly increase
object search or other analysis accuracy by rejecting low-quality
objects and false positives objects, leading to an increase in
precision and recall. Consider the face recognition example
mentioned above. Filtering out non-face object or low quality faces
detected in image data can avoid poor quality or incorrect face
search results. Additionally, the growth rate of the size of the
object recognition index may be slowed to be proportional to the
number of high-quality objects included in the index, in some
embodiments.
[0017] Because filtering detected objects may be performed using
the extracted features determined during the application of object
detection techniques (instead of performing a separate analysis on
the detected objects using different models or recognition
techniques or generating different features for filtering) and can
be applied after the object detection technique is performed, the
computational costs of filtering as a post-processing step can be
minimized. Filtering detected objects for inclusion in an object
recognition index may reduce the amount of noise in the object
recognition index on which the search is performed leading to a
better top-k search accuracy on any size of image index, with
accuracy gains becoming particular notable as number of objects
included in the object recognition index becomes larger (e.g., more
than a million objects), in some embodiments.
[0018] FIG. 1 illustrates a logical diagram of filtering detected
objects from an object recognition index according to extracted
features, according to some embodiments. As indicated at 102, data
for object detection 102 may be received at an object detection
component or pipeline 110 that applies an object detection
technique that includes feature extraction 112. Image data 152, for
example, may be received with no prior annotation or indication of
the contents within image data 152. Data 102 may be received as
part of a request to index (or otherwise include) a particular
image file (or recognized objects therein) or character string
submitted as part of a request, or may be obtained from a data
store that includes a large number of images that may be evaluated
to build or create an object recognition index.
[0019] Object detection 110 may perform operations to prepare data
for object detection (e.g., crop, enhance, down-sample, normalize,
or otherwise modify image data), in some embodiments. For example,
gamma correction may be applied to enhance image data quality for
face detection. Object detection 110 may implement one or more
object detection techniques. For instance, a histogram of oriented
gradients (HOG) determined for an image may be evaluated utilizing
a trained support vector machine (SVM) to detect faces in an area
of image data identified within a bounding box. Similarly, other
object detection techniques may be applied.
[0020] For detected objects, feature extraction 112 may identify
various features within data that correspond to detected objects as
part of object detection 110. For example, feature extraction 112
may be implemented as part of a deep neural network (e.g., a
convolutional neural network (CNN)) which may be trained to
generate feature vectors which, when compared with other feature
vectors generated using the same deep learning model to indicate
similarity between objects according to the respective distance
between the feature vectors, in some embodiments. Feature
extraction 112 may encode or generate extracted features (e.g., as
a feature vector), in various embodiments, which may be used to
represent a detected object. In some embodiments, features may be
extracted using an CNN or other neural network model, and
domain-specific attributes may use the extracted features as
intermediate features from which to extract the domain-specific
attributes as additional features for object recognition. For
example, a bounding box value detected for a recognized object in
image data may be then be used to direct sharpness, brightness, or
other image data specific attributes for the bounding box area
which can be used as additional features (including as features for
indexing criteria as discussed below).
[0021] In the illustrated example, object detection 110 may detect
two objects 154, which may be surrounded by bounding boxes as
detected in image data 152. Because object detection 110 may be
tuned (or implemented separately) for detecting different types of
objects (e.g., human faces, animals, inanimate objects, text,
etc.), the previous examples are not intended to be limiting.
[0022] The features of detected objects 104 determined at object
detection 110 may be filtered according to one or more indexing
criteria at indexing criteria filter 120, in some embodiments. For
example, if object detection 110 performs face detection, then the
extracted features may include face pose features such as pitch,
yaw, and roll. In such a scenario, indexing criteria filter 120 may
apply one or more threshold tests for different ones of the
extracted features, such as pitch of a face pose between
-80.degree. and 80.degree., a yaw of a face pose between
-90.degree. and 90.degree., or a roll of a face pose between
-56.degree. and 56..degree. In some embodiments, some extracted
features may be common to many different types of detect objects.
Brightness, sharpness, or confidence score for the output of the
face detection, for example, may be such commonly extracted
features. Thus, in some embodiments, indexing criteria filter 120
may evaluate extracted features for different types of detected
objects with respect to a brightness greater than a minimum value,
a sharpness greater than a minimum value, a confidence score for
the output of the face detection greater than a minimum value,
and/or various dimensions of a bounding box for the detected
object.
[0023] In various embodiments, detected objects with features that
do not satisfy the indexing criteria filter 120 may be excluded
106. Consider excluded object 156. Various feature values, such as
brightness, bounding box size, or sharpness could have failed to
exceed a minimum threshold value.
[0024] For detected objects that do satisfy the indexing filter
criteria, the detected objects may be added 108 to object
recognition index 130, in some embodiments. Object recognition
index 130 may store representations (e.g., feature vectors, with
features generated from a neural network and/or domain-specific
attributes, or other information descriptive of the indexed
objects) for search or other analysis, as discussed below with
regard to FIG. 5. For example, a feature vector for included object
158 (which may have feature values, such as brightness, bounding
box size, or sharpness that exceeded a minimum threshold value)
could be stored in object recognition index 130.
[0025] Please note that the previous description of filtering
detected objects from an object recognition index according to
extracted features is a logical illustration and thus is not to be
construed as limiting as to the implementation of an object
recognition index, indexing criteria filter, object detection, or
object data.
[0026] This specification begins with a general description of a
provider network that implements multiple different services,
including an object recognition service, which may perform
filtering detected objects from an object recognition index
according to extracted features. Then various examples of,
including different components/modules, or arrangements of
components/module that may be employed as part of implementing the
object recognition service are discussed. A number of different
methods and techniques to implement filtering detected objects from
an object recognition index according to extracted features are
then discussed, some of which are illustrated in accompanying
flowcharts. Finally, a description of an example computing system
upon which the various components, modules, systems, devices,
and/or nodes may be implemented is provided. Various examples are
provided throughout the specification.
[0027] FIG. 2 illustrates an example provider network that may
implement a service that implements an object recognition service
that filters detected objects from an object recognition index
according to extracted features, according to some embodiments.
Provider network 200 may be a private or closed system or may be
set up by an entity such as a company or a public sector
organization to provide one or more services (such as various types
of cloud-based storage) accessible via the Internet and/or other
networks to clients 250, in one embodiment. Provider network 200
may be implemented in a single location or may include numerous
data centers hosting various resource pools, such as collections of
physical and/or virtualized computer servers, storage devices,
networking equipment and the like (e.g., computing system 1000
described below with regard to FIG. 8), needed to implement and
distribute the infrastructure and services offered by the provider
network 200, in one embodiment. In some embodiments, provider
network 200 may implement various computing resources or services,
such as object recognition service 210, storage service(s) 230,
and/or any other type of network-based services 240 (which may
include a virtual compute service and various other types of
storage, database or data processing, analysis, communication,
event handling, visualization, data cataloging, data ingestion
(e.g., ETL), and security services), in some embodiments.
[0028] In various embodiments, the components illustrated in FIG. 2
may be implemented directly within computer hardware, as
instructions directly or indirectly executable by computer hardware
(e.g., a microprocessor or computer system), or using a combination
of these techniques. For example, the components of FIG. 2 may be
implemented by a system that includes a number of computing nodes
(or simply, nodes), each of which may be similar to the computer
system embodiment illustrated in FIG. 8 and described below, in one
embodiment. In various embodiments, the functionality of a given
system or service component (e.g., a component of object
recognition service(s) 210 may be implemented by a particular node
or may be distributed across several nodes. In some embodiments, a
given node may implement the functionality of more than one service
system component (e.g., more than one data store component).
[0029] Object recognition service 210 may implement interface 211
to allow clients (e.g., client(s) 250 or clients implemented
internally within provider network 200, such as a client
application hosted on another provider network service like an
event driven code execution service or virtual compute service) to
index and analyze objects included in data, such as image data
(which may be found in various types of media, such as still images
or video data) or other data (e.g., text/character strings for
natural language processing). For example, object recognition
service 210 may implement interface 211 (e.g., a graphical user
interface, as discussed below with regard to FIG. 4, programmatic
interface that implements Application Program Interfaces (APIs)
and/or a command line interface) may be implemented so that a
client can request an object recognition index be created for image
data 232 stored in storage service(s) 230, and/or image data in
other storage locations within provider network 200 or external to
provider network 200 (e.g., on premise data storage in private
networks). Interface 211 may allow a client to request the
performance of analysis (e.g., to search, compare, classify, or
label image data content), as discussed in detail below.
[0030] Object recognition service 210 may implement a control plane
212 to perform various control operations to implement the features
of object recognition service 210. For example, control plane may
monitor the health and performance of requests at different
components, such as indexing nodes 214 and/or recognition nodes
216. If a node fails, a request fails, or other interruption
occurs, control plane 212 may be able to restart a job to complete
a request (e.g., instead of sending a failure response to the
client). Control plane 212 may, in some embodiments, may arbitrate,
balance, select, or dispatch requests to different node(s) (e.g.,
indexing nodes 214 or recognition nodes 216), in various
embodiments. For example, control plane 212 may receive requests
interface 211 which may be a programmatic interface, and identify
an available node to begin work on the request.
[0031] Object recognition service 210 may implement object indexing
213, as discussed in detail below with regard to FIG. 3. Indexing
nodes(s) 214 may perform various stages, operations, or tasks of
indexing, and/or may operate as individual pipelines or workflows
to perform an entire indexing request (e.g., individually or as a
cluster/group of nodes), in some embodiments.
[0032] Object recognition service 210 may implement object
recognition 215, as discussed in detail below with regard to FIG.
5. Recognition nodes(s) 216 may perform various stages, operations,
or tasks of analyzing data utilizing an object recognition index,
and/or may operate as individual pipelines or workflows to perform
an entire matching request (e.g., individually or as a
cluster/group of nodes), in some embodiments.
[0033] Object recognition store 218 may be one or more data storage
systems or services (e.g., hosted by another provider network 200
service), that can store generated object recognition indexes and
non-index object information to perform object indexing and
recognition as discussed below with regard to FIGS. 3-5.
[0034] Data storage service(s) 230 may implement different types of
data stores for storing, accessing, and managing data on behalf of
clients 250 as a network-based service that enables clients 250 to
operate a data storage system in a cloud or network computing
environment. Data storage service(s) 230 may also include various
kinds relational or non-relational databases, in some embodiments,
Data storage service(s) 230 may include object or file data stores
for putting, updating, and getting data objects or files, in some
embodiments. For example, one data storage service 230 may be an
object-based data store that allows for different data objects of
different formats or types of data, such as structured data (e.g.,
database data stored in different database schemas), unstructured
data (e.g., different types of documents or media content), or
semi-structured data (e.g., different log files, human-readable
data in different formats like JavaScript Object Notation (JSON) or
Extensible Markup Language (XML)) to be stored and managed
according to a key value or other unique identifier that identifies
the object. In at least some embodiments, data storage service(s)
230 may be treated as a data lake. For example, an organization may
generate many different kinds of data, stored in one or multiple
collections of data objects in a data storage service 230. The data
objects in the collection may include related or homogenous data
objects, such as database partitions of sales data, as well as
unrelated or heterogeneous data objects, such as image data files
(e.g., digital photos or video files) audio files and web site log
files. Data storage service(s) 230 may be accessed via programmatic
interfaces (e.g., APIs) or graphical user interfaces.
[0035] Generally speaking, clients 250 may encompass any type of
client that can submit network-based requests to provider network
200 via network 260, including requests for object recognition
service 210 (e.g., a request to search or identify an object using
an object recognition index, etc.). For example, a given client 250
may include a suitable version of a web browser, or may include a
plug-in module or other type of code module that can execute as an
extension to or within an execution environment provided by a web
browser. Alternatively, a client 250 may encompass an application
such as a database application (or user interface thereof), a media
application, an office application or any other application that
may make use of Object recognition service 210 to implement various
applications. In some embodiments, such an application may include
sufficient protocol support (e.g., for a suitable version of
Hypertext Transfer Protocol (HTTP)) for generating and processing
network-based services requests without necessarily implementing
full browser support for all types of network-based data. That is,
client 250 may be an application that can interact directly with
provider network 200. In some embodiments, client 250 may generate
network-based services requests according to a Representational
State Transfer (REST)-style network-based services architecture, a
document- or message-based network-based services architecture, or
another suitable network-based services architecture.
[0036] In some embodiments, a client 250 may provide access to
provider network 200 to other applications in a manner that is
transparent to those applications. Clients 250 may convey
network-based services requests (e.g., access requests to read or
write data may be via network 260, in one embodiment. In various
embodiments, network 260 may encompass any suitable combination of
networking hardware and protocols necessary to establish
network-based-based communications between clients 250 and provider
network 200. For example, network 260 may generally encompass the
various telecommunications networks and service providers that
collectively implement the Internet. Network 260 may also include
private networks such as local area networks (LANs) or wide area
networks (WANs) as well as public or private wireless networks, in
one embodiment. For example, both a given client 250 and provider
network 200 may be respectively provisioned within enterprises
having their own internal networks. In such an embodiment, network
260 may include the hardware (e.g., modems, routers, switches, load
balancers, proxy servers, etc.) and software (e.g., protocol
stacks, accounting software, firewall/security software, etc.)
necessary to establish a networking link between given client 250
and the Internet as well as between the Internet and provider
network 200. It is noted that in some embodiments, clients 250 may
communicate with provider network 200 using a private network
rather than the public Internet.
[0037] FIG. 3 illustrates a logical block diagram for indexing
objects from image data, according to some embodiments. Object
indexing 230 may receive a request to index image data 302. For
example, the request 302 may specify an object recognition index to
include for detected objects, configurations or controls on the
detection technique (e.g., limits on the number/size of objects
detected), a location or identifier of the image, among other
parameters. In some embodiments, the request 302 may be a request
to create an object recognition index which from a set of image
data identified in request 302. In some embodiments, the request
302 may include a parameter to actively perform index filtering
320. If the request 302 were not to include such a parameter value
(or it was set to false, off, etc.) then the index filtering may
not be performed for that request 302 in some embodiments so that
detected objects that would not have satisfied the index filtering
criteria would still be included in the object recognition
index.
[0038] Object detection pipelines 310 may retrieve (or request
other components to retrieve) the specified data 304. As discussed
above with regard to FIG. 1 and below with regard to FIG. 6,
different object detection pipeline(s) 310 may be implemented for
detecting different types of objects, in some embodiments. A face
detection pipeline may be different than a text detection pipeline,
in some embodiments. Various natural language processing techniques
may be implemented as object detection pipelines 310, such as
pipelines implemented to perform different analysis or actions,
including various syntax, semantics, discourse, and speech
analysis. The request to index 302 may specify which pipeline, in
some embodiments. Object detection pipeline(s) 310 may apply object
detection techniques (e.g., utilizing various techniques discussed
above with regard to FIG. 1 such as those that utilize deep neural
networks) to detect objects and extract features 312 which may be
provided to index filtering 320.
[0039] Index filtering 320 may apply a filter corresponding to the
type of object detection pipeline (e.g., a face filter for a face
detection pipeline, a text detection filter for a text detection
filter pipeline, and so on), in some embodiments. As noted earlier,
in some embodiments, indexing criteria may not be linearly applied
but may be weighted in different combinations. For example, for
face detection pipelines alternative sets of criteria may be
satisfied so that satisfying one of the criteria sets may allow the
detected face to be included. For example, one criteria set may be
satisfied by exceeding a minimum threshold of sharpness (e.g., 95%)
and confidence (e.g., 95%), or a second criteria set may be
satisfied by a pose with a pitch value, yaw value, and roll value
within certain ranges, exceeding a minimum brightness value,
exceeding a minimum sharpness value (e.g., which may be different
than the other criteria set, such as >=40%), exceeding a minimum
confidence value (e.g., which may be different than the other
criteria set, such as >=80%), and a bounding box height and
width greater than minimum values. In some embodiments, indexing
filtering criteria could be staged so that a first pass filter may
identify objects to definitively include (or exclude) whereas later
stage indexing criteria could include performing further analysis
of the detected object.
[0040] For those detected objects that satisfy the filter, an
object may be added 336 to object recognition index 330. For
example, a feature vector other representation of the object may be
stored in object recognition store 218 (e.g., as a bit vector or
single data value or alternatively each field may be indexed to a
feature value in an array, field values in a database entry, or
other data structure). For those detected objects that do not
satisfy the filter, an update to a list, structure, or other set of
non-indexed objects 340 (which may be persistently maintained or
periodically purged or trimmed). The update may include the
features (e.g., the feature vector) generated for the excluded
object so that, as discussed below with regard to FIG. 4, the
excluded objects can be returned responsive to interface requests
along with one or more of the extracted feature values, in some
embodiments. Indexing results 306 may be returned which may include
an indication of successful and/or excluded detected objects and
features, in some embodiments.
[0041] Control plane 212 may implement features to manage or
configure the indexing of detected objects, in some embodiments.
For example object detection pipeline deployment 350 allow an
operator to develop, generate, or create a new object detection
pipeline (or update to an existing one) and then push out the
addition/update 352 to resources (e.g., nodes) that implement
object detection pipelines 310. For example, software updates, or
other instructions for performing an additional/updated object
detection pipeline may be stored, booted, or loaded by object
detection pipeline deployment so that requests may be directed to
the updated or additional pipelines. In some embodiments, users of
object recognition service may submit object detection pipelines to
be hosted and performed for an object recognition index. In this
way, users can take advantage of the service platform for handling
indexing requests, storing object recognition indexes and index
filtering 320 without separately implementing these features.
Additionally, specialized object detection pipelines (e.g., for
recognizing specific objects in specific scenarios in video files)
could be deployed by object detection pipeline deployment 350
responsive to such requests. In some embodiments, the custom object
detection pipelines could be limited to requests associated with
identified accounts of provider network 200 or could be publicly
available to any requesting application.
[0042] Control plane 212 may also implement index filtering
management 360 which may update or create new 362 indexing
criteria. In some embodiments, the updates may be triggered by an
update to an existing object detection pipeline 310 or addition of
a new object detection pipeline 352. Changes or new indexing
criteria may be determined based on analysis of false and true
positives detected by the updated or additional object detection
pipeline, as discussed in detail below with regard to FIG. 7. For
example, if a new object detection pipeline 310 is added then index
filtering 310 may be updated to include a filter 320 specific to
the object detection pipeline 310, or threshold values for an
existing filter may be modified, in some embodiments. Indexing
filtering criteria may be stored in a data store (not illustrated)
so that when a detect object is received from an object detection
pipeline, the index filtering criteria for that pipeline may be
retrieved an applied. Updates, therefore, may be made to the
criteria in the data store, in some embodiments.
[0043] FIG. 4 illustrates an example interface for displaying
indexing results, according to some embodiments. A graphical user
interface, such as indexing interface 400 may be implemented as
part of interface 211 of object recognition service 201, in some
embodiments. Indexing interface 400 may provide a display area for
image data, such as image data 400 that may be evaluated or
considered for object detection and inclusion in an object
recognition index. Overlaid upon image data 410 may be indications
of detected objects and such indications may include whether the
object was (or was not) included in the object recognition index,
such as indexed object indication 412 and non-indexed object 414.
For example, a bounding box may be displayed around detected
objects. If the bounding box is selected via an I/O action (e.g., a
mouse click, a touch gesture, etc.), then the features of the
detected object may be displayed.
[0044] For example, non-indexed object feature(s) 440 may include a
display area for features of the non-indexed object in order to
provide insight into the values that could have prevented inclusion
of the object 414. For example, feature values for a detected face
object 414 (and unsatisfied thresholds, in some cases) could be
displayed, like pose value(s) 442a (which may describe the rotation
of the face inside the image data), sharpness value 442b,
brightness value 442c, bounding box value(s) 442d (which may
describe coordinates of the bounding box that surrounds the face in
the image data from which bounding box size can be determined) and
confidence value 442e (which may describe a level of confidence
that the bounding box contains a face), in some embodiments. An
indication of the failed threshold or indexing criteria could be
provided, in some embodiments. For example, extracted features 442
that failed or contributed to the failure of the object to the
index could be highlighted.
[0045] Please note that although FIG. 4 is discussed in the context
of a graphical user interface, various features for indicated
indexed and non-index objects could be implemented for other
interfaces (e.g., APIs or command line interfaces). For example, a
request may be received via an API to return excluded or otherwise
unindexed objects detected in image data submitted for inclusion
for an object recognition index, in some embodiments.
[0046] The object recognition index created according to the
techniques described above can be used in different analyses. FIG.
5 illustrates a logical block diagram of object recognition using
an object recognition index, according to some embodiments. Object
search 510 may be implemented in some embodiments using an object
recognition index, like object recognition index 540. Object search
510 may take as an input to object analysis request 502, like a
query image for searching stored image data to see if the image is
found or a query text/character string that matches one written by
a same writer. For example, a video file catalog could be searched
for a particular actor according to query image of the actors face,
in some embodiments.
[0047] Object comparison 520 may be implemented, in some
embodiments, to perform analysis to compare detected objects with
indexed objects. For example, facial recognition could be
implemented by comparing a live face image captured in streaming
video data with an index face object to determine the identity and
thus the permissions of the person whose face is being captured in
the live image data. A feature vector may be generated for the live
face image using a CNN or other feature extraction technique that
is the same as was applied to generate object recognition index
540, in some embodiments.
[0048] Object content analysis 530 could be used to search stored
image data according to content, such as text, types of objects
according to appearance (e.g., red apples), or restricted content
(e.g., adult content). For example, the feature vector generated
for the query object may be compared with the feature vectors of
objects in the object recognition index 540. If the distance
between the feature vectors is less than a threshold, then the
object in the index may be included in a result 504.
[0049] Object analysis request 502 may indicate which analysis to
perform, as well as the object recognition index 540 to use. In
this way, object recognition 215 can get or update object
attributes 542 back 544 from object recognition index, in some
embodiments. A result 504 of the object analysis may then be
returned (e.g., locations of video files with identified actor, an
indication of a user match, an indication or label for the content,
like "red apple."
[0050] Although FIGS. 2-5 have been described and illustrated in
the context of a provider network implementing an object
recognition service, the various components illustrated and
described in FIGS. 2-5 may be easily applied to other object
recognition systems that utilize an object recognition index to
perform various types of object analyses. As such, FIGS. 2-5 are
not intended to be limiting as to other embodiments of filtering
detected objects from an object recognition index according to
extracted features.
[0051] FIG. 6 illustrates a high-level flowchart of various methods
and techniques to implement filtering detected objects from an
object recognition index according to extracted features, according
to some embodiments. Various different systems and devices may
implement the various methods and techniques described below,
either singly or working together. Therefore, the above examples
and or any other systems or devices referenced as performing the
illustrated method, are not intended to be limiting as to other
different components, modules, systems, or devices.
[0052] As indicated at 610, data to add objects detected in the
data to an object recognition index may be received, in some
embodiments. For example, the data may be retrieved from an
identified storage location, or may be streamed, sent, or
transferred to an object detection system as part of or alongside a
request to index objects detected within the data, such as the
requests discussed above with regard to FIG. 3. The object
recognition index may be identified according to an identifier or
other indicator, in some embodiments. In this way, different object
recognition indexes built from different image data and utilized
for different analyses can be maintained.
[0053] As indicated at 620, an object detection technique may be
applied on the data to detect an object within the data, in some
embodiments. Different techniques may be performed to crop,
enhance, down-sample, normalize, or otherwise modify data for
object detection, in some embodiments. For example, gamma
correction may be applied to enhance image data quality for face
detection. Different object detection techniques may be applied for
different types of data and analyses. For instance, a histogram of
oriented gradients (HOG) determined for an image may be evaluated
utilizing a trained support vector machine (SVM) to detect faces in
an area of image data identified within a bounding box. Similarly,
other object detection techniques may be applied, such as
rule-based object detection, structural feature detection, template
matching, neural networks, sparse network of winnows, naive bayes
classifiers, hidden markov models, or inductive learning-based
detection techniques may be performed to detect objects.
[0054] For detected objects, various features within the data that
corresponds to detected objects may be extracted. For instance, the
image data within a bounding box or other boundary for the detected
object may be then be analyzed according to a feature extraction
technique, like a CNN. The feature extraction technique may
identify features of the detected object so that if included in the
object recognition index, the detected object could be analyzed for
a match, in some embodiments. Extracted features may be encoded
(e.g., as a feature vector), in various embodiments, which may be
used to represent a detected object.
[0055] As indicated at 630, the features of the detected object
determined as part of the application of the object detection
technique may be evaluated with respect to one or more indexing
criteria to exclude objects from the object recognition index that
do not satisfy the indexing criteria, in some embodiments. For
example, various combinations of thresholds, ranges, confidence
scores, Boolean values, or other features extracted along with the
detected object may be compared. In some embodiments, a single
composite or weighted score may be generated, while in other
embodiments, individual evaluations for individual criterion of the
indexing filter criteria may be performed. In some circumstances
all indexing criteria may be need to be satisfied, whereas in other
embodiments, alternative criteria can be satisfied.
[0056] As indicated by the positive exit from 640, a detected
object that satisfies the indexing criteria may be included in the
object recognition index, as indicated at 660, in some embodiments.
A feature vector or other representation of the detected object may
be stored in a data store, structure, or other location that can be
analyzed when performing an analysis on detected objects in the
object recognition index. As indicated by the positive exit from
640, a detected object that does not satisfy the indexing criteria
may be excluded in the object recognition index, as indicated at
650, in some embodiments. A response indicating an error, or a
detected false positive object or low quality object may be sent
(e.g. via an API, graphical interface, etc.). In some embodiments,
the response may indicate the values of the extracted features for
the objects and/or the indexing criteria that the object failed to
satisfy.
[0057] FIG. 7 illustrates a high-level flowchart of various methods
and techniques to implement determining indexing criteria to filter
detected objects, according to some embodiments. As indicated at
710, an update to or addition of an object detection technique
applied to detect objects in data may be performed, in some
embodiments. For example, one or more convolution or down-sampling
layers of a neural network used to detect objects may be adjusted
or replaced to improve detection performance. In some embodiments,
an update to the object detection technique may include a change to
pre-processing or other formatting performed upon data prior to
evaluating the data through the deep neural network. An addition of
a new object detection technique may be trained to detect a type of
object, such as human faces, animals, text, vehicles, among other
examples that was not previously supported in an object detection
or recognition system, like object recognition service 210 in FIG.
2.
[0058] As indicated at 720, respective features determined for true
and falsely detected objects according to the updated or added
object detection technique applied to a set of data may be
obtained, in some embodiments. For example, a labeled set of
objects (e.g., true positive object detections and false positive
object detections) may be received for the set of image data. The
updated or added technique may also be applied to the image set of
data to extract feature values for the detected objects (e.g.,
sharpness, bounding box, confidence, etc.) at an indexing filtering
management system or component, in some embodiments.
[0059] As indicated at 730, a predictive model may be trained
according to the obtained features to identify one or more
respective feature value(s) that maximize the prediction of a true
detected object upon application of the predictive model to the
feature(s) of the true detected object, in some embodiments. For
example, a boosting technique, like gradient boosting, may be
performed to generate a predictive model like a decision tree that
identifies applies weak learners determined from individual feature
values to generate one or more combined sets of feature values that
are indicative of a true positive detection of an object. For
example, the statistical values of the minimum, maximum, mean,
standard deviation, outlier minimum and outlier maximum of
different feature values for true positive object detections can be
used as a starting point for determining the feature values by
applying logic such as greater than the minimum and/or less than
the maximum of each of the different statistical values (e.g.,
sharpness greater than the minimum value).
[0060] Once trained, the predictive model may be used to identify
updates (if any) to the indexing criteria. As indicated at 740,
indexing criteria for excluding detected objects from an object
recognition index may be updated based on the respective features
identified by the predictive model that maximize the prediction of
true detected objects, in some embodiments. For instance, the
minimum confidence value, sharpness value, or brightness value may
be adjusted upward or downward according to the boosted decision
tree.
[0061] The methods described herein may in various embodiments be
implemented by any combination of hardware and software. For
example, in one embodiment, the methods may be implemented on or
across one or more computer systems (e.g., a computer system as in
FIG. 8) that includes one or more processors executing program
instructions stored on one or more computer-readable storage media
coupled to the processors. The program instructions may implement
the functionality described herein (e.g., the functionality of
various servers and other components that implement the
network-based virtual computing resource provider described
herein). The various methods as illustrated in the figures and
described herein represent example embodiments of methods. The
order of any method may be changed, and various elements may be
added, reordered, combined, omitted, modified, etc.
[0062] Embodiments of filtering detected objects from an object
recognition index according to extracted features as described
herein may be executed on one or more computer systems, which may
interact with various other devices. One such computer system is
illustrated by FIG. 8. In different embodiments, computer system
1000 may be any of various types of devices, including, but not
limited to, a personal computer system, desktop computer, laptop,
notebook, or netbook computer, mainframe computer system, handheld
computer, workstation, network computer, a camera, a set top box, a
mobile device, a consumer device, video game console, handheld
video game device, application server, storage device, a peripheral
device such as a switch, modem, router, or in general any type of
computing device, computing node, compute node, or electronic
device.
[0063] In the illustrated embodiment, computer system 1000 includes
one or more processors 1010 coupled to a system memory 1020 via an
input/output (I/O) interface 1030. Computer system 1000 further
includes a network interface 1040 coupled to I/O interface 1030,
and one or more input/output devices 1050, such as cursor control
device 1060, keyboard 1070, and display(s) 1080. Display(s) 1080
may include standard computer monitor(s) and/or other display
systems, technologies or devices. In at least some implementations,
the input/output devices 1050 may also include a touch- or
multi-touch enabled device such as a pad or tablet via which a user
enters input via a stylus-type device and/or one or more digits. In
some embodiments, it is contemplated that embodiments may be
implemented using a single instance of computer system 1000, while
in other embodiments multiple such systems, or multiple nodes
making up computer system 1000, may host different portions or
instances of embodiments. For example, in one embodiment some
elements may be implemented via one or more nodes of computer
system 1000 that are distinct from those nodes implementing other
elements.
[0064] In various embodiments, computer system 1000 may be a
uniprocessor system including one processor 1010, or a
multiprocessor system including several processors 1010 (e.g., two,
four, eight, or another suitable number). Processors 1010 may be
any suitable processor capable of executing instructions. For
example, in various embodiments, processors 1010 may be
general-purpose or embedded processors implementing any of a
variety of instruction set architectures (ISAs), such as the x86,
PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In
multiprocessor systems, each of processors 1010 may commonly, but
not necessarily, implement the same ISA.
[0065] In some embodiments, at least one processor 1010 may be a
graphics processing unit. A graphics processing unit or GPU may be
considered a dedicated graphics-rendering device for a personal
computer, workstation, game console or other computing or
electronic device. Modern GPUs may be very efficient at
manipulating and displaying computer graphics, and their highly
parallel structure may make them more effective than typical CPUs
for a range of complex graphical algorithms. For example, a
graphics processor may implement a number of graphics primitive
operations in a way that makes executing them much faster than
drawing directly to the screen with a host central processing unit
(CPU). In various embodiments, graphics rendering may, at least in
part, be implemented by program instructions that execute on one
of, or parallel execution on two or more of, such GPUs. The GPU(s)
may implement one or more application programmer interfaces (APIs)
that permit programmers to invoke the functionality of the GPU(s).
Suitable GPUs may be commercially available from vendors such as
NVIDIA Corporation, ATI Technologies (AMD), and others.
[0066] System memory 1020 may store program instructions and/or
data accessible by processor 1010. In various embodiments, system
memory 1020 may be implemented using any suitable memory
technology, such as static random access memory (SRAM), synchronous
dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other
type of memory. In the illustrated embodiment, program instructions
and data implementing desired functions, such as those described
above are shown stored within system memory 1020 as program
instructions 1025 and data storage 1035, respectively. In other
embodiments, program instructions and/or data may be received, sent
or stored upon different types of computer-accessible media or on
similar media separate from system memory 1020 or computer system
1000. Generally speaking, a non-transitory, computer-readable
storage medium may include storage media or memory media such as
magnetic or optical media, e.g., disk or CD/DVD-ROM coupled to
computer system 1000 via I/O interface 1030. Program instructions
and data stored via a computer-readable medium may be transmitted
by transmission media or signals such as electrical,
electromagnetic, or digital signals, which may be conveyed via a
communication medium such as a network and/or a wireless link, such
as may be implemented via network interface 1040.
[0067] In one embodiment, I/O interface 1030 may coordinate I/O
traffic between processor 1010, system memory 1020, and any
peripheral devices in the device, including network interface 1040
or other peripheral interfaces, such as input/output devices 1050.
In some embodiments, I/O interface 1030 may perform any necessary
protocol, timing or other data transformations to convert data
signals from one component (e.g., system memory 1020) into a format
suitable for use by another component (e.g., processor 1010). In
some embodiments, I/O interface 1030 may include support for
devices attached through various types of peripheral buses, such as
a variant of the Peripheral Component Interconnect (PCI) bus
standard or the Universal Serial Bus (USB) standard, for example.
In some embodiments, the function of I/O interface 1030 may be
split into two or more separate components, such as a north bridge
and a south bridge, for example. In addition, in some embodiments
some or all of the functionality of I/O interface 1030, such as an
interface to system memory 1020, may be incorporated directly into
processor 1010.
[0068] Network interface 1040 may allow data to be exchanged
between computer system 1000 and other devices attached to a
network, such as other computer systems, or between nodes of
computer system 1000. In various embodiments, network interface
1040 may support communication via wired or wireless general data
networks, such as any suitable type of Ethernet network, for
example; via telecommunications/telephony networks such as analog
voice networks or digital fiber communications networks; via
storage area networks such as Fibre Channel SANs, or via any other
suitable type of network and/or protocol.
[0069] Input/output devices 1050 may, in some embodiments, include
one or more display terminals, keyboards, keypads, touchpads,
scanning devices, voice or optical recognition devices, or any
other devices suitable for entering or retrieving data by one or
more computer system 1000. Multiple input/output devices 1050 may
be present in computer system 1000 or may be distributed on various
nodes of computer system 1000. In some embodiments, similar
input/output devices may be separate from computer system 1000 and
may interact with one or more nodes of computer system 1000 through
a wired or wireless connection, such as over network interface
1040.
[0070] As shown in FIG. 8, memory 1020 may include program
instructions 1025, that implement the various methods and
techniques as described herein, and data storage 1035, comprising
various data accessible by program instructions 1025. In one
embodiment, program instructions 1025 may include software elements
of embodiments as described herein and as illustrated in the
Figures. Data storage 1035 may include data that may be used in
embodiments. In other embodiments, other or different software
elements and data may be included.
[0071] Those skilled in the art will appreciate that computer
system 1000 is merely illustrative and is not intended to limit the
scope of the techniques as described herein. In particular, the
computer system and devices may include any combination of hardware
or software that can perform the indicated functions, including a
computer, personal computer system, desktop computer, laptop,
notebook, or netbook computer, mainframe computer system, handheld
computer, workstation, network computer, a camera, a set top box, a
mobile device, network device, internet appliance, PDA, wireless
phones, pagers, a consumer device, video game console, handheld
video game device, application server, storage device, a peripheral
device such as a switch, modem, router, or in general any type of
computing or electronic device. Computer system 1000 may also be
connected to other devices that are not illustrated, or instead may
operate as a stand-alone system. In addition, the functionality
provided by the illustrated components may in some embodiments be
combined in fewer components or distributed in additional
components. Similarly, in some embodiments, the functionality of
some of the illustrated components may not be provided and/or other
additional functionality may be available.
[0072] Those skilled in the art will also appreciate that, while
various items are illustrated as being stored in memory or on
storage while being used, these items or portions of them may be
transferred between memory and other storage devices for purposes
of memory management and data integrity. Alternatively, in other
embodiments some or all of the software components may execute in
memory on another device and communicate with the illustrated
computer system via inter-computer communication. Some or all of
the system components or data structures may also be stored (e.g.,
as instructions or structured data) on a computer-accessible medium
or a portable article to be read by an appropriate drive, various
examples of which are described above. In some embodiments,
instructions stored on a non-transitory, computer-accessible medium
separate from computer system 1000 may be transmitted to computer
system 1000 via transmission media or signals such as electrical,
electromagnetic, or digital signals, conveyed via a communication
medium such as a network and/or a wireless link. Various
embodiments may further include receiving, sending or storing
instructions and/or data implemented in accordance with the
foregoing description upon a computer-accessible medium.
Accordingly, the present invention may be practiced with other
computer system configurations.
[0073] It is noted that any of the distributed system embodiments
described herein, or any of their components, may be implemented as
one or more web services. In some embodiments, a network-based
service may be implemented by a software and/or hardware system
designed to support interoperable machine-to-machine interaction
over a network. A network-based service may have an interface
described in a machine-processable format, such as the Web Services
Description Language (WSDL). Other systems may interact with the
web service in a manner prescribed by the description of the
network-based service's interface. For example, the network-based
service may describe various operations that other systems may
invoke, and may describe a particular application programming
interface (API) to which other systems may be expected to conform
when requesting the various operations.
[0074] In various embodiments, a network-based service may be
requested or invoked through the use of a message that includes
parameters and/or data associated with the network-based services
request. Such a message may be formatted according to a particular
markup language such as Extensible Markup Language (XML), and/or
may be encapsulated using a protocol such as Simple Object Access
Protocol (SOAP). To perform a web services request, a network-based
services client may assemble a message including the request and
convey the message to an addressable endpoint (e.g., a Uniform
Resource Locator (URL)) corresponding to the web service, using an
Internet-based application layer transfer protocol such as
Hypertext Transfer Protocol (HTTP).
[0075] In some embodiments, web services may be implemented using
Representational State Transfer ("RESTful") techniques rather than
message-based techniques. For example, a web service implemented
according to a RESTful technique may be invoked through parameters
included within an HTTP method such as PUT, GET, or DELETE, rather
than encapsulated within a SOAP message.
[0076] The various methods as illustrated in the FIGS. and
described herein represent example embodiments of methods. The
methods may be implemented in software, hardware, or a combination
thereof. The order of method may be changed, and various elements
may be added, reordered, combined, omitted, modified, etc.
[0077] Various modifications and changes may be made as would be
obvious to a person skilled in the art having the benefit of this
disclosure. It is intended that the invention embrace all such
modifications and changes and, accordingly, the above description
to be regarded in an illustrative rather than a restrictive
sense.
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