Filtering detected objects from an object recognition index according to extracted features

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.

BACKGROUND

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.

DETAILED DESCRIPTION OF EMBODIMENTS

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.

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).

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.

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.

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.

FIG. 1illustrates a logical diagram of filtering detected objects from an object recognition index according to extracted features, according to some embodiments. As indicated at102, data for object detection102may be received at an object detection component or pipeline110that applies an object detection technique that includes feature extraction112. Image data152, for example, may be received with no prior annotation or indication of the contents within image data152. Data102may 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.

Object detection110may 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 detection110may 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.

For detected objects, feature extraction112may identify various features within data that correspond to detected objects as part of object detection110. For example, feature extraction112may 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 extraction112may 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).

In the illustrated example, object detection110may detect two objects154, which may be surrounded by bounding boxes as detected in image data152. Because object detection110may 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.

The features of detected objects104determined at object detection110may be filtered according to one or more indexing criteria at indexing criteria filter120, in some embodiments. For example, if object detection110performs face detection, then the extracted features may include face pose features such as pitch, yaw, and roll. In such a scenario, indexing criteria filter120may apply one or more threshold tests for different ones of the extracted features, such as pitch of a face pose between −80° and 80°, a yaw of a face pose between −90° and 90°, or a roll of a face pose between −56° and 56.° 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 filter120may 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.

In various embodiments, detected objects with features that do not satisfy the indexing criteria filter120may be excluded106. Consider excluded object156. Various feature values, such as brightness, bounding box size, or sharpness could have failed to exceed a minimum threshold value.

For detected objects that do satisfy the indexing filter criteria, the detected objects may be added108to object recognition index130, in some embodiments. Object recognition index130may 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 toFIG. 5. For example, a feature vector for included object158(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 index130.

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.

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.

FIG. 2illustrates 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 network200may 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 clients250, in one embodiment. Provider network200may 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 system1000described below with regard toFIG. 8), needed to implement and distribute the infrastructure and services offered by the provider network200, in one embodiment. In some embodiments, provider network200may implement various computing resources or services, such as object recognition service210, storage service(s)230, and/or any other type of network-based services240(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.

In various embodiments, the components illustrated inFIG. 2may 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 ofFIG. 2may 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 inFIG. 8and 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)210may 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).

Object recognition service210may implement interface211to allow clients (e.g., client(s)250or clients implemented internally within provider network200, 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 service210may implement interface211(e.g., a graphical user interface, as discussed below with regard toFIG. 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 data232stored in storage service(s)230, and/or image data in other storage locations within provider network200or external to provider network200(e.g., on premise data storage in private networks). Interface211may 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.

Object recognition service210may implement a control plane212to perform various control operations to implement the features of object recognition service210. For example, control plane may monitor the health and performance of requests at different components, such as indexing nodes214and/or recognition nodes216. If a node fails, a request fails, or other interruption occurs, control plane212may be able to restart a job to complete a request (e.g., instead of sending a failure response to the client). Control plane212may, in some embodiments, may arbitrate, balance, select, or dispatch requests to different node(s) (e.g., indexing nodes214or recognition nodes216), in various embodiments. For example, control plane212may receive requests interface211which may be a programmatic interface, and identify an available node to begin work on the request.

Object recognition service210may implement object indexing213, as discussed in detail below with regard toFIG. 3. Indexing nodes(s)214may 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.

Object recognition service210may implement object recognition215, as discussed in detail below with regard toFIG. 5. Recognition nodes(s)216may 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.

Object recognition store218may be one or more data storage systems or services (e.g., hosted by another provider network200service), that can store generated object recognition indexes and non-index object information to perform object indexing and recognition as discussed below with regard toFIGS. 3-5.

Data storage service(s)230may implement different types of data stores for storing, accessing, and managing data on behalf of clients250as a network-based service that enables clients250to operate a data storage system in a cloud or network computing environment. Data storage service(s)230may also include various kinds relational or non-relational databases, in some embodiments, Data storage service(s)230may include object or file data stores for putting, updating, and getting data objects or files, in some embodiments. For example, one data storage service230may 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)230may 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 service230. 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)230may be accessed via programmatic interfaces (e.g., APIs) or graphical user interfaces.

Generally speaking, clients250may encompass any type of client that can submit network-based requests to provider network200via network260, including requests for object recognition service210(e.g., a request to search or identify an object using an object recognition index, etc.). For example, a given client250may 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 client250may 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 service210to 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, client250may be an application that can interact directly with provider network200. In some embodiments, client250may 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.

In some embodiments, a client250may provide access to provider network200to other applications in a manner that is transparent to those applications. Clients250may convey network-based services requests (e.g., access requests to read or write data may be via network260, in one embodiment. In various embodiments, network260may encompass any suitable combination of networking hardware and protocols necessary to establish network-based-based communications between clients250and provider network200. For example, network260may generally encompass the various telecommunications networks and service providers that collectively implement the Internet. Network260may 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 client250and provider network200may be respectively provisioned within enterprises having their own internal networks. In such an embodiment, network260may 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 client250and the Internet as well as between the Internet and provider network200. It is noted that in some embodiments, clients250may communicate with provider network200using a private network rather than the public Internet.

FIG. 3illustrates a logical block diagram for indexing objects from image data, according to some embodiments. Object indexing230may receive a request to index image data302. For example, the request302may 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 request302may be a request to create an object recognition index which from a set of image data identified in request302. In some embodiments, the request302may include a parameter to actively perform index filtering320. If the request302were 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 request302in some embodiments so that detected objects that would not have satisfied the index filtering criteria would still be included in the object recognition index.

Object detection pipelines310may retrieve (or request other components to retrieve) the specified data304. As discussed above with regard toFIG. 1and below with regard toFIG. 6, different object detection pipeline(s)310may 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 pipelines310, such as pipelines implemented to perform different analysis or actions, including various syntax, semantics, discourse, and speech analysis. The request to index302may specify which pipeline, in some embodiments. Object detection pipeline(s)310may apply object detection techniques (e.g., utilizing various techniques discussed above with regard toFIG. 1such as those that utilize deep neural networks) to detect objects and extract features312which may be provided to index filtering320.

Index filtering320may 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.

For those detected objects that satisfy the filter, an object may be added336to object recognition index330. For example, a feature vector other representation of the object may be stored in object recognition store218(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 objects340(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 toFIG. 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 results306may be returned which may include an indication of successful and/or excluded detected objects and features, in some embodiments.

Control plane212may implement features to manage or configure the indexing of detected objects, in some embodiments. For example object detection pipeline deployment350allow an operator to develop, generate, or create a new object detection pipeline (or update to an existing one) and then push out the addition/update352to resources (e.g., nodes) that implement object detection pipelines310. 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 filtering320without 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 deployment350responsive to such requests. In some embodiments, the custom object detection pipelines could be limited to requests associated with identified accounts of provider network200or could be publicly available to any requesting application.

Control plane212may also implement index filtering management360which may update or create new362indexing criteria. In some embodiments, the updates may be triggered by an update to an existing object detection pipeline310or addition of a new object detection pipeline352. 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 toFIG. 7. For example, if a new object detection pipeline310is added then index filtering310may be updated to include a filter320specific to the object detection pipeline310, 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.

FIG. 4illustrates an example interface for displaying indexing results, according to some embodiments. A graphical user interface, such as indexing interface400may be implemented as part of interface211of object recognition service201, in some embodiments. Indexing interface400may provide a display area for image data, such as image data400that may be evaluated or considered for object detection and inclusion in an object recognition index. Overlaid upon image data410may 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 indication412and non-indexed object414. 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.

For example, non-indexed object feature(s)440may 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 object414. For example, feature values for a detected face object414(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 value442b, brightness value442c, 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 value442e(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 features442that failed or contributed to the failure of the object to the index could be highlighted.

Please note that althoughFIG. 4is 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.

The object recognition index created according to the techniques described above can be used in different analyses.FIG. 5illustrates a logical block diagram of object recognition using an object recognition index, according to some embodiments. Object search510may be implemented in some embodiments using an object recognition index, like object recognition index540. Object search510may take as an input to object analysis request502, 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.

Object comparison520may 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 index540, in some embodiments.

Object content analysis530could 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 index540. If the distance between the feature vectors is less than a threshold, then the object in the index may be included in a result504.

Object analysis request502may indicate which analysis to perform, as well as the object recognition index540to use. In this way, object recognition215can get or update object attributes542back544from object recognition index, in some embodiments. A result504of 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.”

AlthoughFIGS. 2-5have been described and illustrated in the context of a provider network implementing an object recognition service, the various components illustrated and described inFIGS. 2-5may be easily applied to other object recognition systems that utilize an object recognition index to perform various types of object analyses. As such,FIGS. 2-5are not intended to be limiting as to other embodiments of filtering detected objects from an object recognition index according to extracted features.

FIG. 6illustrates 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.

As indicated at610, 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 toFIG. 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.

As indicated at620, 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, naïve bayes classifiers, hidden markov models, or inductive learning-based detection techniques may be performed to detect objects.

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.

As indicated at630, 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.

As indicated by the positive exit from640, a detected object that satisfies the indexing criteria may be included in the object recognition index, as indicated at660, 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 from640, a detected object that does not satisfy the indexing criteria may be excluded in the object recognition index, as indicated at650, 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.

FIG. 7illustrates a high-level flowchart of various methods and techniques to implement determining indexing criteria to filter detected objects, according to some embodiments. As indicated at710, 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 service210inFIG. 2.

As indicated at720, 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.

As indicated at730, 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).

Once trained, the predictive model may be used to identify updates (if any) to the indexing criteria. As indicated at740, 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.

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 inFIG. 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.

In the illustrated embodiment, computer system1000includes one or more processors1010coupled to a system memory1020via an input/output (I/O) interface1030. Computer system1000further includes a network interface1040coupled to I/O interface1030, and one or more input/output devices1050, such as cursor control device1060, keyboard1070, and display(s)1080. Display(s)1080may include standard computer monitor(s) and/or other display systems, technologies or devices. In at least some implementations, the input/output devices1050may 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 system1000, while in other embodiments multiple such systems, or multiple nodes making up computer system1000, 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 system1000that are distinct from those nodes implementing other elements.

System memory1020may store program instructions and/or data accessible by processor1010. In various embodiments, system memory1020may 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 memory1020as program instructions1025and data storage1035, 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 memory1020or computer system1000. 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 system1000via I/O interface1030. 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 interface1040.

As shown inFIG. 8, memory1020may include program instructions1025, that implement the various methods and techniques as described herein, and data storage1035, comprising various data accessible by program instructions1025. In one embodiment, program instructions1025may include software elements of embodiments as described herein and as illustrated in the Figures. Data storage1035may include data that may be used in embodiments. In other embodiments, other or different software elements and data may be included.

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.