Patent Publication Number: US-9836481-B2

Title: Image-based retrieval and searching

Description:
PRIORITY CLAIM UNDER 35 U.S.C. 119(e) 
     This application claims the benefit of priority to U.S. Provisional Patent Application Ser. No. 61/972,932, filed Mar. 31, 2014, which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     Embodiments of the present disclosure relate generally to data processing and, more particularly, but not by way of limitation, to image processing. 
     BACKGROUND 
     Images can be used to convey information efficiently or in a way not possible with text, particularly from the viewpoint of a user viewing the images on an electronic device. Images can be retrieved from memory of the electronic device or received over the Internet as part of a website. The images can include a wealth of information that can be used in a variety of applications. For example, an example image can be sent as a part of a search query to request information related to other images that are similar to the example image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various ones of the appended drawings merely illustrate example embodiments of the present disclosure and cannot be considered as limiting its scope. 
         FIG. 1  is a block diagram illustrating a networked system, according to some example embodiments. 
         FIG. 2  is a block diagram illustrating an example embodiment of the image retrieval system of  FIG. 1  including multiple modules forming at least a portion of the client-server system of  FIG. 1 . 
         FIG. 3  is a block diagram illustrating an example embodiment of the image processor module of  FIG. 2  including multiple modules forming at least a portion of the client-server system of  FIG. 1 . 
         FIGS. 4 and 5  are interface diagrams illustrating example user interfaces of a web resource with multiple display elements delivered to the user device by the image retrieval system, according to an example embodiment. 
         FIG. 6  is a block diagram illustrating an example data memory system including a number of data structures of the image retrieval system, in accordance with an example embodiment. 
         FIG. 7  is a flowchart illustrating an example method of processing an image-based search query, in accordance with an example embodiment. 
         FIGS. 8 and 9  are diagrams illustrating schematically examples of geometric descriptor representations, in accordance with an example embodiment. 
         FIG. 10  is a block diagram illustrating an example processing pipeline formed with a number of modules of  FIG. 2 , in accordance with an example embodiment. 
         FIG. 11  is a block diagram illustrating schematically an example of processing data to generate angle bin data, according to some example embodiments. 
         FIGS. 12A and 12B  are diagrams illustrating an example of processing image data to generate geometric descriptor data, according to some example embodiments. 
         FIG. 13  is a block diagram illustrating an example of a software architecture that may be installed on a machine, according to some example embodiments. 
         FIG. 14  illustrates a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment. 
     
    
    
     The headings provided herein are merely for convenience and do not necessarily affect the scope or meaning of the terms used. 
     DETAILED DESCRIPTION 
     The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail. 
     In various example embodiments, an image retrieval system provides to users image-based search services that allow users to provide images, in addition or alternative to textual search terms, as part of a search query request. Image-based search services offer functionality by capturing visual attributes, which may be difficult to describe using only words, and by providing convenience for searching without typing. Accordingly, the image retrieval system can receive image data (also referred herein as a “query image”) as part of search query request. The query image corresponds to an image file, a portion of an image file, an IP network address, and/or any data usable to locate an image file or a portion of an image file over a network in various embodiments. The image retrieval system processes the query image and returns search results that correspond to query image (e.g., one or more images similar to the query image) and/or information associated with similar image data (e.g., product information related to products having similar visual attributes of the query image). 
     Proliferation of large-scale image collections on web has made the task of efficient image retrieval challenging. Given a query image, one example aspect, among others of some embodiments, is to retrieve images of the same object or scene from a large scale database with high accuracy, speed, and low memory usage. One issue is how to concisely represent the visual information present in images. For example, an example aspect is to reasonably trade off computational efficiency and retrieval accuracy. 
     Various example embodiments described herein use a geometric Vector of Linearly Aggregated Descriptors (VLAD) process that encodes a set of local feature descriptors computed from one or more images. The feature descriptors are encoded in a way that incorporates weak geometric cues of the image. For instance, the encoding of gVLAD incorporates angle information of image features of the image that are associated with the local feature descriptors. In one aspect, gVLAD can have a technical effect to provide compact and accurate representations of images and can be scaled to billions of feature descriptors (by avoiding expensive hard disk operations) while retaining retrieval performance. 
     Local feature descriptors (such as a number of “descriptor vectors”) can be computed using a number of suitable feature detectors, such as Speeded Up Robust Features (SURF), Scale-invariant Feature Transform (SIFT), and/or the like feature detectors. By way of background, the feature detector identifies a number of keypoints and determines a descriptor vector for each keypoint. An example of a keypoint includes a point that corresponds to a point of a region of the image that contains high quality information, such as a line, edge, corner, blob, and/or the like points of interest within the image. One example way to determine a keypoint is to compute a gradient field over the region (e.g., at multiple scales) and identify the keypoints as points represented by a significant change in gradient or a significant magnitude of a gradient. Here, significant includes meaning being above a threshold value. In example embodiments, the feature detector can determine for each keypoint a histogram of magnitudes and a direction (also referred to herein as an “angle” or a “keypoint angle”). The magnitudes, for example, can correspond to (e.g., magnitude of the edge, for example, strength of edge). The direction, for example, can correspond to the angle or orientation of the dominant edge (based on magnitude of edge) across multiple scales at the keypoint. The local feature descriptor of each keypoint corresponds to a vector of the values of the histogram associated with the correspond keypoint. As stated, SURF and/or SIFT detectors generate both the feature descriptor and the angle of each keypoint of an image. 
     While example embodiments are described herein as being deployed within a network environment, it will be appreciated that the systems and methods disclosed herein can be deployed for local image-based searching and retrieval. For example, a user can use an image of a friend to search data storage of the user&#39;s personal computer to locate image files including images of the friend. Other similar uses are contemplated for alternative embodiments. 
     With reference to  FIG. 1 , an example embodiment of a high-level client-server-based network architecture  100  is shown. A networked system  102 , in the example forms of a network-based marketplace or payment system, provides server-side functionality via a network  104  (e.g., the Internet or wide area network (WAN)) to one or more user device  110  (also referred to as a “client device”).  FIG. 1  illustrates, for example, a web client  112  (e.g., a browser, such as the Internet Explorer® browser developed by Microsoft® Corporation of Redmond, Wash. State), client application(s)  114 , and a programmatic client  116  executing on user device  110 . 
     The user device  110  may comprise, but are not limited to, a mobile phone, desktop computer, laptop, portable digital assistants (PDAs), smart phones, tablets, ultra books, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, or any other communication device that a user may utilize to access the networked system  102 . In some embodiments, the user device  110  may comprise a display module (not shown) to display information (e.g., in the form of user interfaces). In further embodiments, the user device  110  may comprise one or more of a touch screen, accelerometer, gyroscope, camera, microphone, global positioning system (GPS) device, and so forth. The user device  110  may be a device that is used by a user to perform a transaction involving digital items within the networked system  102 . In one embodiment, the networked system  102  is a network-based marketplace that responds to requests for product listings, publishes publications comprising item listings of products available on the network-based marketplace, and manages payments for these marketplace transactions. One or more user  106  may be a person, a machine, or other means of interacting with user device  110 . In embodiments, the user  106  is not part of the network architecture  100 , but may interact with the network architecture  100  via user device  110  or another means. For example, one or more portions of network  104  may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a WiFi network, a WiMax network, another type of network, or a combination of two or more such networks. 
     Each user device  110  may include one or more applications (also referred to as “apps”) such as, but not limited to, a web browser, messaging application, electronic mail (email) application, an e-commerce site application (also referred to as a marketplace application), and the like. In some embodiments, if the e-commerce site application is included in a given user device  110 , then this application is configured to locally provide the user interface and at least some of the functionalities with the application configured to communicate with the networked system  102 , on an as-needed basis, for data and/or processing capabilities not locally available (e.g., access to a database of items available for sale, to authenticate a user, to verify a method of payment, etc.). Conversely, if the e-commerce site application is not included in the user device  110 , the user device  110  may use its web browser to access the e-commerce site (or a variant thereof) hosted on the networked system  102 . 
     One or more users  106  may be a person, a machine, or other means of interacting with the user device  110 . In example embodiments, the user  106  is not part of the network architecture  100 , but may interact with the network architecture  100  via the user device  110  or other means. For instance, the user  106  provides input (e.g., touch screen input or alphanumeric input) to the user device  110  and the input is communicated to the networked system  102  via the network  104 . In this instance, the networked system  102 , in response to receiving the input from the user  106 , communicates information to the user device  110  via the network  104  to be presented to the user  106 . In this way, the user  106  interacts with the networked system  102  using the user device  110 . 
     An application program interface (API) server  120  and a web server  122  are coupled to, and provide programmatic and web interfaces respectively to, one or more application server  140 . The application server(s)  140  may host one or more publication system  142  and payment system  144 , each of which may comprise one or more modules or applications and each of which may be embodied as hardware, software, firmware, or any combination thereof. The application server(s)  140  are, in turn, shown to be coupled to one or more database server  124  that facilitate access to one or more information storage repositories or database(s)  126 . In an example embodiment, the database(s)  126  is a storage device that stores information to be posted (e.g., publications or listings) to the publication system(s)  142 . The database(s)  126  may also store digital item information in accordance with example embodiments. 
     Additionally, a third party application  132 , executing on third party server(s)  130 , is shown as having programmatic access to the networked system  102  via the programmatic interface provided by the API server  120 . For example, the third party application  132 , utilizing information retrieved from the networked system  102 , supports one or more features or functions on a website hosted by the third party. The third party website, for example, provides one or more promotional, marketplace, or payment functions that are supported by the relevant applications of the networked system  102 . 
     The publication system(s)  142  may provide a number of publication functions and services to a user  106  that accesses the networked system  102 . The payment system(s)  144  may likewise provide a number of functions to perform or facilitate payments and transactions. While the publication system(s)  142  and payment system(s)  144  are shown in  FIG. 1  to both form part of the networked system  102 , it will be appreciated that, in alternative embodiments, each system  142  and  144  may form part of a payment service that is separate and distinct from the networked system  102 . In some embodiments, the payment system(s)  144  may form part of the publication system  142 . 
     The image retrieval system  150  provides functionality operable to perform image-based services using the user provided data. For example, the image retrieval system  150  receives image query requests, accesses an image data set from the databases  126 , and returns the query results. The image retrieval system  150  will be described in greater detail in connection with  FIGS. 2, 3, 7, and 9 . 
     Further, while the client-server-based network architecture  100  shown in  FIG. 1  employs a client-server architecture, the present inventive subject matter is of course not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system, for example. The various publication system(s)  142 , payment system(s)  144 , and image retrieval system  150  could also be implemented as standalone software programs, which do not necessarily have networking capabilities. 
     The web client  112  may access the various publication and payment systems  142  and  144  via the web interface supported by the web server  122 . Similarly, the programmatic client  116  accesses the various services and functions provided by the publication and payment systems  142  and  144  via the programmatic interface provided by the API server  120 . The programmatic client  116  may, for example, be a seller application (e.g., the Turbo Lister application developed by eBay® Inc., of San Jose, Calif.) to enable sellers to author and manage listings on the networked system  102  in an off-line manner, and to perform batch-mode communications between the programmatic client  116  and the networked system  102 . 
       FIG. 2  is a block diagram illustrating an example embodiment of the image retrieval system of  FIG. 1  including multiple modules forming at least a portion of the client-server system of  FIG. 1 . The modules  210 - 250  of the illustrated image retrieval system  150  include an application interface module(s)  210 , a data storage interface module(s)  220 , a feature detector module(s)  230 , an image processor module(s)  240 , and a search engine module(s)  250 . In some embodiments, the components of the image retrieval system  150  are included in the application server(s)  140  of  FIG. 1 . However, it will be appreciated that in alternative embodiments, one or more components of the image retrieval system  150  described below are included, additionally or alternatively, in other devices, such as one or more of the user device  110  and/or the third party server(s)  130  of  FIG. 1 . It will also be appreciated that the image retrieval system  150  is deployed in systems other than online marketplaces in alternative embodiments. 
     The modules  210 - 250  of the image retrieval system  150  are hosted on dedicated or shared server machines that are communicatively coupled to enable communications between server machines. One or more of the modules  210 - 250  are deployed in one or more datacenters. Each of the modules  210 - 250  is communicatively coupled (e.g., via appropriate interfaces) to the other modules  210 - 250  and to various data sources, so as to allow information to be passed between the modules  210 - 250  of the image retrieval system  150  or so as to allow the modules  210 - 250  to share and access common data. The various modules  210 - 250  of the image retrieval system  150  furthermore access one or more database  126  via the database server(s)  124 . 
     The application interface module(s)  210  is a hardware-implemented module that facilitates communication of data between the image retrieval system  150  and the user device  110 , the third-party server(s)  130 , and other devices connected to the network  104 . For instance, the application interface module(s)  210  provides data communication interface with one or more of the API server  120 , the web server  122 , and the database server  124 . Over these communication interfaces, the application interface module(s)  210  receives image-based query requests and returns search results. In example embodiments, the application interface module(s)  210  also receives image data to include into an image dataset. Furthermore, the application interface module(s)  210  can read and write data to the database(s)  126 . 
     In operation, the application interface module(s)  210  receives an image-based query request from the user device  106 . The query request can include a query image. For example, the query request can directly include the image data or can include reference data usable to locate the image data with the client-server system  100 . In response to receiving the request, the image retrieval system  150  processes the request and provides the search results for display on the user device  110 . The search results include an indication of one or more stored images. Examples of the indication include the image data and/or reference data usable to locate the image data with the client-server system  100 . The search results can also include data indicative of or associated with the one or more images. 
     The data storage interface module(s)  220  is a hardware-implemented module that facilitates accessing data for the image retrieval system  150 . In an example embodiment, the data storage interface module(s)  220  interfaces with the database(s)  126  of  FIG. 1  to access stored image data. In an illustrative example embodiment, a number of data structures accessible by the data storage interface module(s)  220  will be described in greater detail in connection with  FIG. 6 . 
     The feature detector module(s)  230  is a hardware-implemented module that facilitates generating first feature description data of a first type from image data of an image query request provided by a remote device, such as the user device  110 . In example embodiments, the first feature description data includes components (e.g., descriptor vectors) and corresponding angles. Each of the first plurality of components is indicative of an image feature of the image data. Each of the corresponding angles represents an orientation of the image feature indicated by the corresponding component. 
     In example embodiments, the feature detector module includes at least one of a Speeded Up Robust Feature (SURF) detector or a Scale-invariant Feature Transform (SIFT) detector to generate the first feature description data from the image data of the image query request. 
     The corresponding angles represent angles in the image space of the image data of the image query request. 
     The image processor module(s)  240  is a hardware-implemented module that facilitates processing the feature description data generated by the feature detector module(s)  230 . For instance, the image processor module(s)  240  encodes the feature description data to a second type of feature description data suitable for image-based searching. The second type corresponds to a VLAD representation with aggregated descriptors for each pair of the feature codes and the angle bins, as will described in greater detail below and in connection with  FIG. 3 . 
     In operation, the image processor module(s)  240  accesses feature codes and angle bins stored, e.g., in the database  126 , in response to receiving feature description data from the feature detector module(s)  230 . The feature codes collectively correspond to a codebook of feature descriptor vectors Ci for i=1, . . . , K. The angle bins correspond to data suitable to partition a circle in M sectors (e.g., thus, creating M angle bins) for grouping the angles of the feature description data from the feature detector module(s)  230  into M possible groups. The angle bins can correspond to numerical values representing the angle bins or can correspond to data defining a membership function. The angle bin data will be described below in connection with  FIG. 3 . 
     As described in greater detail in connection with  FIG. 3 , the image processor module(s)  240  utilizes the feature codes as a bag-of-words (BOW) vocabulary of visual features that are utilized to describe an image (e.g., a query image or an inventory image). Each feature code is a representative descriptor that is learned from the local feature descriptors extracted from inventory images (e.g., an image dataset stored in the database  126 ). The feature detector module(s)  230  can extract the local feature descriptors. An example process of generating the codebook will be described below in connection with  FIG. 3 . 
     In response to the receiving new local feature descriptor from the feature detector module(s)  230 , the image processor module(s)  240  maps the new local feature descriptor using feature codes to generate a signature histogram. The X-axis of the histogram may correspond to each word in the BOW model defined by the feature codes. In addition, a difference of the new local feature descriptor from each code of the feature codes is computed, such as performed in VLAD. However, the angles associated with the respective new feature descriptors are assigned to one of the multiple angle bins. The angle bin data can be learned from the inventory images and stored in the database  126 , as stated. For example, keypoints within a cluster may be assigned to 4 bins that are respectively associated with 0-90 degrees, 91-180 degrees, 181-270 degrees, and 271-359 degrees. Then, a VLAD difference vector may be computed for each angle bin (e.g., gVLAD) to preserve the geometric information. These representations that are generated from each angle bin may then be combined to generate a gVLAD difference vector, as described in greater detail below in connection with  FIG. 3 . 
     The image processor module(s)  240  will be described in greater detail later in connection with  FIGS. 3, 7, and 10 . 
     The search module(s)  250  is a hardware-implemented module that facilitates searching and selecting one or more stored inventory images. For instance, in operation, the search engine module selects an inventory image stored in the database based on the feature description data that is generated by the image processor module(s)  240  in response to an image query request. The search module(s)  250  will be described in greater detail later in connection with  FIGS. 7 . 
     Example methods of operation of the modules  210 - 250  will be described in greater detail later in connection with  FIGS. 7 and 10 . 
       FIG. 3  is a block diagram illustrating an example embodiment of the image processor module(s)  240  of  FIG. 2  including multiple modules  310 - 330  forming at least a portion of the client-server system  100  of  FIG. 1 . The modules  310 - 330  of the illustrated image processor module(s)  240  include a codebook adaption module(s)  310 , a geometric descriptor module(s)  320 , and a whitening module(s)  330 . The modules  310 - 330  of the image processor module(s)  240  are hosted on dedicated or shared server machines that are communicatively coupled to enable communications between server machines. One or more of the modules  310 - 330  are deployed in one or more datacenters. Each of the modules  310 - 330  is communicatively coupled (e.g., via appropriate interfaces) to the other modules  310 - 330  and to various data sources, so as to allow information to be passed between the modules  310 - 330  of the image processor module(s)  240  or so as to allow the modules  310 - 330  to share and access common data. The various modules  310 - 330  of the image processor module(s)  240  furthermore access one or more database  126  via the database server(s)  124 . 
     The geometric descriptor module(s)  320  is a hardware-implemented module that facilitates encoding the feature description data generated by the feature detector module(s)  230  into feature description data of a different type, such a gVLAD vector type, suitable for facilitating efficient image retrieval. 
     For instance, as stated, the feature detector module(s)  230  generates a local feature descriptor x (e.g. a SURF or SIFT descriptor vector), which is a d-dimensional vector. Codebook or feature codes may be denoted as μ=[μ 1 , μ 2 , . . . , μ K ], where K represents the number of feature codes (e.g., the size of the codebook) according to an embodiment. Let NN(x) represent the nearest-neighbor function that maps an input local feature descriptor x to its nearest visual word index i where 1≦i≦K, according to an embodiment. 
     A stated, the feature detector module(s)  230  also provides angle information of the local feature descriptor x. The geometric descriptor module(s)  320  encodes such angle information of the descriptor x for efficient image matching, according to an embodiment. Accordingly, the feature detector module(s)  230  provides the feature description data x θ , where x still represents the local feature descriptor and the angle θ represents the angle of the local feature descriptor x, e.g., the dominant angle of the keypoint associated with the local feature descriptor x. In other words, the angle θ represents the orientation or angle of the feature in the image space. The angle θ can be computed as the dominant direction of gradient within a local window of the keypoint. 
     To model the distribution of angles, a clustering approach can be used. A membership function ψ over the angles is defined as:
 
ψ(θ( x )):0≦θ&lt;2π→{1,2, . . . , M},   (1)
 
where the index M denotes the number of angle bins.
 
     The gVLAD vector ν i   j  for i th  of the K feature codes (also referred to as “feature bins”) and j th  of the M angular bins is represented as: 
                     v   i   j     =     {               ∑         x   θ     ⁢     :     ⁢           ⁢     NN   ⁡     (   x   )         =   i       ⁢           ⁢     x   θ       -     μ   i               if   ⁢           ⁢     ψ   ⁡     (   θ   )         =   j               0   d             if   ⁢           ⁢     ψ   ⁡     (   θ   )         ≠   j                     (   2   )               
where d is the dimension of feature vector of local feature descriptor x. The term x θ −μ i , represents a residual of x θ  taken with reference to the feature code μ i . The contribution of each visual word V i  in the geometric VLAD can now, according to an embodiment, be formed by combining individual contributions from each angle bin:
 
 V =[ν i   1   ,ν   i   2   , . . . ,ν   i   M−1   ,ν   i   M ]  (3)
 
where V i  is a row vector with size of dM. The gVLAD representation V is defined by accumulating contributions of from all K visual words, and has D dimensions (D=KdM:
 
 V =[ V   1   ,V   2   , . . . ,V   K−1   ,V   K ]  (4)
 
     According, the second feature description data (e.g., the gVLAD representation V) comprise a plurality of components (e.g., each ν i   j ). Each component corresponds to an aggregation of residuals of selected components of the first plurality of components (local descriptor vector x). The selection is based on the nearest neighbor function NN(x)=i. The residual is taken with reference to a corresponding one of the feature codes (e.g., the feature code μ i ). Furthermore, the selected components are selected also based at least on comparing the corresponding angles of the plurality of angles with the angle bins via the membership evaluations of Equation 2. 
     Additionally, in example embodiments, geometric descriptor module(s)  320  uses Z-score based normalization. Normalization can facilitate effective and correct measurements of distances between vector representation. For example, the geometric descriptor module(s)  320  uses three-stage normalization in an example embodiment. First, the geometric descriptor module(s)  320  uses the intra-normalization, where the sum of residuals of each visual word ν i   j  is L2 normalized (e.g., the Euclidean norm) independently, where 1≦i≦K and 1≦j≦M. This first normalization is followed by an “inter-Z-score” normalization across different visual words, according to an embodiment. Given a vector X, its Z-score normalization is computed as: 
                 X   -   μ     σ     ,         
where μ and σ represent the mean and standard deviation of X. The t th  entry of V i  is denoted as ν i,t , where V i  may be defined in Equation 3 according to an embodiment. The geometric descriptor module(s)  320  applies the inter-Z-score normalization on each [ν 1,t , ν 2,t , . . . , ν i,t , . . . , ν K,t ,], where 1≦t≦M×d and 1≦i≦K. Third, the geometric descriptor module(s) 320 normalizes the result of the inter-Z-score normalization using the L2 norm—e.g., V:=V/∥V∥ 2 .
 
     The codebook adaptation module(s)  310  is a hardware-implemented module that facilitates the generating and updating a codebook (e.g., the feature codes) as well as generating and updating the angle bin data. In an example aspect of addressing the overhead of iterative codebook training on large scale dataset at real-time, the codebook adaptation module(s)  310  operates incrementally in an example embodiment. For example, the number of inventory images stored in the database  126  can grow continuously, which in turn can lead to performing codebook training processes to update the codebook. The codebook adaptation module(s)  310 , according to an example embodiment, adapts the existing codebook with the image data of the new portion of the image dataset, which inhibits frequent large-scale codebook training. Secondly, the codebook adaptation module(s)  310  can have the technical effect of allowing codebook training from diverse datasets as a codebook trained on one dataset (e.g., Paris building images) can be adapted to retrieve images from another dataset (e.g., holiday images). 
     As stated, the codebook adaptation module(s)  310  generates angle bin data. In an example embodiment, the codebook adaptation module(s)  310  utilizes membership functions ψ(θ) to perform angle binning. For instance, the codebook adaptation module(s)  310  utilizes a mixture of Von-Mises distributions for computing the membership function of a dataset of inventory images. One example way to generate the membership function ψ(θ) is to apply clustering over the angle distribution and find the appropriate membership assignments for each angle value among M learned clusters, according to an embodiment. Angles can have a circular distribution of in the range of [0, 2π), whereas existing clustering algorithms that based on L2 distance, such as k-means, assume a Cartesian co-ordinate space for input data, and cannot be applied directly. To address this issue, the codebook adaptation module(s)  310  represents each keypoint as (r, θ), where r is the radial coordinate, according to an embodiment. Since the image processing module(s)  240  uses the angle θ but not directly the radius r, the codebook adaptation module(s)  310  fixes the radius r as an arbitrary number r&gt;0, according to an embodiment. The codebook adaptation module(s)  310  performs a non-linear transform from this polar co-ordinate to 2D Cartesian co-ordinate space, according to an embodiment, using the trigonometric functions:
 
 x=r ×cos θ  (5)
 
 y=r ×sin θ  (6)
 
Thus, each angle θ is mapped to a point z(θ)=(x, y) in this 2-d space, according to an embodiment. To learn the membership of function ψ(θ), according to an embodiment, the codebook adaptation module(s)  310  performs k-means clustering in this space satisfying:
 
                     argmin     {       α   1     ,   …   ⁢           ,     α   M       }       ⁢       ∑     i   =   1     M     ⁢           ⁢       ∑       z   j     ∈     Ξ   i         ⁢           ⁢              z   j     -     α   i            2                 (   7   )               
where α i , is the cluster centroid by averaging all points in cluster set Ξ i . As an example, the cluster set Ξ i  is a set of (all) points which share the same closest centroid among all centroids. The codebook adaptation module(s)  310  calculates the membership of each angle θ according to the follow equation:
 
ψ(θ)=arg min iε{1,2 . . . M}   ∥z (θ)−α i ∥ 2   (8)
 
     As stated, the codebook adaptation module(s)  310  additionally or alternatively generates and/or updates the codebook (e.g., feature codes). As an illustrative example embodiment, a source dataset of images is represented by the set S, and an initial codebook μ=[μ 1 , μ 2 , . . . , μ K ] is generated in connection with the set S. In response to receiving a new image dataset T to be added to the inventory images, the codebook adaptation module(s)  310  adapts μ to generate an updated codebook {circumflex over (μ)}. For instance, the updated codebook {circumflex over (μ)} can be determined in accordance with the following equation: 
                           μ   ^     i     =       1   N     ⁢       ∑     t   =   1     N     ⁢           ⁢       γ   i     ⁡     (   t   )             ,         x   θ     ⁡     (   t   )       ∈   T       ⁢     
     ⁢   where           (   9   )                   γ   i     ⁡     (   t   )       =     {             x   θ     ⁡     (   t   )               if   ⁢           ⁢     NN   ⁡     (       x   θ     ⁡     (   t   )       )         =     μ   i                 0   d             if   ⁢           ⁢     NN   ⁡     (       x   θ     ⁡     (   t   )       )         ≠     μ   i                       (   10   )               
and where N is the total number of feature descriptors in dataset T and x θ  (t) represents t th  descriptor, according to an embodiment. The initial codebook μ can be trained using the set S. For other different datasets, the updated codebook  μ   i  can used in conjunction with Equation 2 to compute the representation of the gVLAD.
 
     The whitening module(s)  330  is a hardware-implemented module that facilitates whitening of the output description data of the geometric descriptor module(s)  320 . For example embodiments with a large collection of images, the size of representation can be carefully considered so as to be feasible for practical real time retrieval. For instance, using only 256 visual words with 64 dimensional SURF descriptors and 4 angle bins generates a feature representation of size D=64×256×4=65,536, according to an embodiment. To achieve memory-efficient representation of this vector, the image processor  240  can use the whitening module(s)  330  to reduce the dimensions of the representation. For instance, the whitening module(s)  330  uses Principal Component Analysis (PCA) with pre-whitening. The PCA whitening matrix can be expressed in the form of:
 
 P=D   −1/2   E   T   (11)
 
where EDE T =E{ VV T} is the eigenvector decomposition of the covariance matrix of the (zero mean) data  V , where each row  V   1 =V 1 −V 0 , and V 0  is the mean vector computed from all gVLAD representation vectors according to an embodiment. D=diag [d 1 , d 2 , . . . , d D ] is the D×D diagonal matrix containing the eigenvalues and E=[e 1 , e 2 , . . . , e D ] is an orthogonal N×D matrix having the eigenvectors as columns according to an embodiment. The obtained whitened gVLAD representation may be:
 
 {tilde over (V)}   l   =P (:,1:ρ) T   × V     l   (12)
 
where ρ is the number of eigenvcctors to keep, i.e. the dimension of reduced feature vectors. {tilde over (V)} l  may then be L2 normalized according to an embodiment. The complete algorithm is outlined in Algorithm 1, according to an embodiment.
 
     A summary of the algorithm is outlined in Algorithm 1, according to an embodiment. 
     
       
         
           
               
             
               
                   
               
               
                 Algorithm 1 Computation of gVLAD descriptor, 
               
               
                 according to an embodiment 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                  1: 
                 S1: Keypoint detection and descriptor: compute image descriptors x θ , 
               
               
                   
                 where x is the appearance vector, and θ represents the angle. 
               
               
                  2: 
                 S2: Generate visual vocabulary [μ 1 , μ 2 , . . ., μ K ] using k-means on all 
               
               
                   
                 descriptors from training data. 
               
               
                  3: 
                 S3: Learning membership function ψ(θ) for each x θ   
               
               
                  4: 
                   arg min i∈{1,2,..   .,M}  ∥z(θ) − α i ∥ 2   
               
               
                  5: 
                 S4: Compute geometric VLAD v i   j : 
               
               
                   
               
               
                  6: 
                 
                   
                     
                       
                         
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                  7: 
                   V i  = [v i   1 , v i   2 ,..., v i   M−1 , v i   M ] 
               
               
                  8: 
                   V = [V 1 , V 2 ,..., V K−1 , V K ] 
               
               
                  9: 
                 S5: Codebook Adaptation: 
               
               
                   
               
               
                 10: 
                   
             μ   ^     i     =       1   N     ⁢       ∑     t   =   1     N     ⁢           ⁢       γ   i     ⁡     (   t   )             ,         x   θ     ⁢           ⁢     (   t   )       ∈   T         
 
               
               
                   
               
               
                 11: 
                 S6: intra-normalization, Inter-Z-score normalization, and L2 
               
               
                   
                 normalization. 
               
               
                 12: 
                 S7: PCA whitening 
               
               
                 13: 
                   {tilde over (V)} l  = P(:,1: ρ) T  ×  V   l   
               
               
                   
               
            
           
         
       
     
       FIGS. 4 and 5  are interface diagrams illustrating example user interfaces of a web resource with multiple display elements delivered to the user device  110  by the image retrieval system  150 , according to an example embodiment. As used herein, a web resource corresponds to data and/or code delivered to the user device  110  over the network  104  to render a webpage, or to be processed and/or rendered by a software application executing on the user device  110 . The user interfaces described below can correspond to search services within an online marketplace in an example embodiment. It will be appreciated that in alternative embodiments that the user interfaces can correspond to any web resource or standalone (e.g., non-networked based) application providing image-based search services. 
       FIG. 4  is an interface diagram illustrating a user interface  400  including the user device  110  rendering a search frame  410 . For example, the image retrieval system  150  provides the search frame  410  to the user device  110  in response to the user device  110  requesting or accessing a web resource that provides image retrieval services supported by the image retrieval system  150 . Examples of the requesting or accessing of the web resource includes launching a software application to be executed on the user device  110  and/or requesting a webpage that interfaces with the image retrieval system  150 . User input received by the search frame  410  from the user  106  is transmitted to the image retrieval system  150 . 
     In the illustrated example embodiment of  FIG. 4 , the search frame  410  includes interface element  412  (e.g., an input text box) for receiving user input to generate an image query request. The interface element  412  receives data that includes image data or a link (e.g., a URL address) to image data. Furthermore, the search frame  410  includes control elements  416 ,  418 . The control element  418  is user-selectable to cause the user device  110  to provide user interfaces (not shown) to select an image file for uploading to the image retrieval system  150 . The control element  418  is user-selectable to cause the user device  110  to provide the image retrieval system  150  an image query request including the user input provided in the interface element  412 . An example method of processing the request will be described in greater detail later in connection with  FIG. 7 . 
       FIG. 5  is an interface diagram illustrating a user interface  500  including the user device  110  rendering a frame  510  of the web resource providing image-based search results. For example, the frame  510  is presented to the user  106  in response to the image retrieval system  150  receiving an image query request from the user device  110 , processing the request (e.g., as will be described in connection with  FIG. 7 ), and providing the results to the user device  110  for display. As an illustrative example, the frame  510  displays the search results of an example image query request including image data of a book. The images  512 A-C are images selected by the image retrieval system  150  from the inventory images stored in the database  126  based on having image features similar to the image features of the image query request. The user device  110  displays the results as a one dimensional list, although the results can be arranged in a number of other ways, such as a two dimension grid, in alternative embodiments. Additional information (e.g., description, product information, pricing, availability, and so forth) can be provided in text boxes  514 A-C for the respective images  512 A-C. 
       FIG. 6  is a block diagram illustrating an example data memory system, such as the database  126 , including a number of data structures  602 ,  604  of the image retrieval system  150 , in accordance with an example embodiment. In operations, the data storage interface module(s)  220  provides the feature detector module(s)  230  and the image processor module(s)  240  access to the data structures  602 ,  604 . 
     The database  126  includes a search representation data structure  602  and an image inventory data structure  604 . The search representation data structure  602  includes N feature description data structures  610 A-N, where N is the number of images in the image inventory (e.g., image dataset), a feature codes data field  612 , an angle bins data field  614 , and a whitening data field  616 . The image inventory data structure  604  includes N stored image data fields  620 A-N. The feature description data structures  610 A-N each represents the gVLAD vector (e.g., Equations 2-4 and/or 12) and each are linked to a corresponding stored image data field  620 A-N. It will be appreciated that the gVLAD vectors of the feature description data structures  610 A-N can be the normalized and/or whitened version of the gVLAD vectors as described above in connection with  FIG. 3 . 
     The feature codes data field  612  store data representing the feature codes (e.g., a codebook) generated from the image inventory. For example, the codebook adaptation module(s)  310  generates and stores the feature codes data filed  612 . The angle bins data field  614  stores data representing the angle bins generated for the image inventory. For example, the codebook adaptation module(s)  310  generates and stores the angle bins data filed  612  (e.g., Equation 8). The whitening data field  616  stores data representing whitening data (e.g., Equation 12). Each of the stored image data fields  620 A-N corresponds to image data (an image file or an addressable reference to an image file). 
     It will be appreciated that the data of the database  126  can be stored together or separately in a number of data storage devices by one or more components of the client-server-based network architecture  100 . The data storage interface module(s)  220  ( FIG. 2 ) of the image retrieval system  150  accesses the database  126 . 
       FIG. 7  is a flowchart illustrating an example method  700  of processing an image-based search query, in accordance with an example embodiment. Query processing comprising receiving a query request in the form of an image, comparing the received image with stored images by comparing their gVLAD feature, and returning matching images as search results. 
     In this example, the method  700  includes operations such as generating first feature description data of a first type (block  710 ), accessing feature codes and angle bins (block  720 ), generating second feature description data of a second type (block  730 ), selecting a first one of a plurality of stored feature descriptions data (block  740 ), and providing an indication of a stored image (block  750 ). The example method  700  will be described below, by way of explanation, as being performed by certain modules. It will be appreciated, however, that the operations of the example method  700  can be performed in any suitable order by any number of the modules shown in  FIGS. 2 and 3 . 
     In an example embodiment, the method  700  starts at block  710 , in which the feature detection module(s)  230  generates first feature description data of a first type from image data of an image query request provided by a remote device, such as the user device  110 . The first feature description data can correspond to SURF or SIFT descriptors x including keypoint angle data θ. As such, the first feature description data includes a first plurality of components and corresponding angles. Further, each of the first plurality of components is indicative of an image feature (e.g., a keypoint) of the image data. Each of the corresponding angles represents an orientation of the image feature indicated by the corresponding component. Thus the angles are the orientation of the image feature in the image space, and not, for example, the angle of the descriptor vector in the feature space. 
     As stated, the image retrieval system  150  can receive the image query request from the remote device. In an example embodiment, the image query request is generated by the user interface  400  of  FIG. 4  based on user input and the remote device transmits the image query request to the image retrieval system  150 . 
     At block  720 , the image processor module(s)  240  uses the data storage interface module(s)  220  to access feature codes i and angle bins w stored in the feature codes data field  612  and angle bins data field  614 . At block  730 , the image processor module(s)  240  uses the geometric descriptor module(s)  320  to generate second feature description data of a second type. The second feature description data can include a gVLAD vector V representation of the local feature descriptors x of the image of the image query request. For example, the geometric descriptor module(s)  320 , in accordance with Equations 2-4, compares a plurality of groups with respective codes of the feature codes. Accordingly, each of the plurality of groups comprises at least of portion of the first plurality of components that is determined based at least on comparing (e.g., via the nearest neighbor function NN(x)) the first plurality of components with the feature codes and comparing (e.g., via the angle bins/membership function ψ) the corresponding angles with the angle bins. Furthermore, in example embodiments, the geometric descriptor module(s)  320  performs the three-stage normalization described above in connection with  FIG. 3 . Furthermore, in example embodiments, the geometric descriptor module(s)  320  whitens the second feature description data in accordance with Equations 11 and 12 described above in connection with  FIG. 3 . 
     At block  740 , a search engine module(s)  250  selects a first one of a plurality of stored feature description data (e.g., feature description data fields  610 A-N that each includes a gVLAD vector) linked to respective stored images (e.g., stored image data fields  620 A-N). The search engine module(s)  250  selects the first stored feature description data based at least on comparing the second feature description data and the first stored feature description data. For example, the search engine module(s)  250  can compute the Euclidean distance (or using any suitable norm) between the gVLAD vector V with the stored feature description data. 
     In example embodiments, the search engine module(s)  250  selects the images that are associated with a distance that is less than a predetermined threshold in search results. Images can also be ranked based on the distance in ascending order. 
     At block  750 , an application interface module(s)  210  provides an indication of the stored image linked to the first stored feature description data for display of the stored image linked to the first stored feature description data on the remote device. After block  750 , the method  700  ends. 
       FIGS. 8 and 9  are diagrams illustrating schematically examples of geometric descriptor representations, in accordance with an example embodiment. Elements common to  FIGS. 8 and 9  share common reference indicia, and only differences between the figures are described herein for the sake of brevity. 
     With reference to  FIG. 8 , the feature codes C 1 -C 5  partition a feature space  800 . Additionally, the feature space includes the descriptor vectors X 1 -X 4 , each located a distance R from the nearest neighbor feature code C 1 . Each of the descriptor vectors X 1 -X 4  are shown with an arrow to represent the orientation θ associated with each vector X 1 -X 4 . Furthermore, angle bins B 1  and B 2  are shown. Accordingly, vectors X 1  and X 3  grouped in the angle bin B 2 , and vectors X 2  and X 4  are grouped with angle bin B 1 . Thus, the gVLAD vector V has two components associate with the feature code C 1 . The first component is the residual associated with the first bin B 1  and its value is 2R. The second component is the residual associated with the second bin B 1  and its value is 2R. 
       FIG. 9  shows the same feature space  800  of  FIG. 8  but with different orientations of the descriptor vectors X 1 -X 4 . Each of the vectors X 1 -X 4  belong to the angle bin B 1 . Thus, the first component is the residual associated with the first bin B 1  and its value is 4R. The second component is the residual associated with the second bin B 1  and its value is 0. Accordingly, the gVLAD can allow images to be differentiated based on orientation information. 
       FIG. 10  is a block diagram illustrating an example processing pipeline  1000  formed with a number of modules of  FIGS. 2 and 3 , in accordance with an example embodiment. The pipeline  1000  performs gVLAD feature computation for generating the gVLAD representation for each inventory image. The results of the pipeline  1000  can be stored in the data structures  602 ,  604 . 
     The feature detection module(s)  230  receives a number of images of an image inventory and extracts keypoint data. Keypoints can be extracted from all images in an inventory of images (e.g., given 100 images in an inventory and each associated with 10 keypoints, in total 1000 keypoints will be generated). As stated, each keypoint corresponds to a descriptor vector x and a direction θ. For example, the gradient of a point (e.g., directional change in the intensity of color) in an image (e.g., pixel) may be described with a vector x. At each keypoint, the angle θ points in the direction of largest intensity change, and the gradient vector may correspond to the rate of change in that direction. A vector x can be described according to technologies including SIFT (e.g., 128 dimensions) or SURF (e.g., 64 dimensions). 
     The codebook adaption modules(s)  310  receives the vectors x and angles θ of the complete image inventory and generates a set of feature codes p. For example, the set of extracted feature descriptor vectors x are clustered and each cluster center is used as a representative visual word to form a vocabulary. The size of codebook is the number of cluster centers. 
     Furthermore, the codebook adaption modules(s)  310  learns the angle bins (e.g., membership functions ψ). For example, angles associated with the descriptors from each keypoint are clustered to form a certain number of bins. The number of the clusters can be automatically learned. The membership of a given angle may be determined by the closest angle bin.  FIG. 11  shows an illustrative example. 
     In the case that there is an existing set of feature codes, and the received image data corresponds to a new set of images to be added to the existing inventory of images, the codebook adaptation module(s)  310  updates the set of feature codes as described in connection with  FIG. 3 . 
     The geometric descriptor module(s)  320  computes the gVLAD vector Vi to each of the feature codes μi in the learned set of feature codes. The geometric descriptor module(s)  320  accumulates (sums) the distances to form the final feature vector (Vi:=Vi+(X−Ci), for each X). In order to preserve the geometry information, the geometric descriptor module(s)  320  computes the VLAD vector in each angle bin Bi, and combines all the accumulated distance vectors to form the final feature vector, as described in connection with  FIG. 3 . As stated, the geometric descriptor module(s)  320  can normalize and/or whiten (e.g., via the whitening module(s)  330 ) the gVLAD vector data. Normalization can serve to effectively and correctly measure the distance between vector representations. PCA with pre-whitening can serve to achieve a memory efficient representation of the final feature vector generated by gVLAD. For each image in an inventory, the gVLAD vector V is associated with the given image, as shown in  FIG. 6 . 
       FIG. 11  is a block diagram  1100  illustrating schematically an example of processing data to generate angle bin data, according to some example embodiments. For example, an example angle distribution  1110  is computed from 8.3 million descriptors from an image dataset. The Von Mises model is learned from the distribution  1110  and used to predict the angle membership yr of each descriptor, according to an embodiment. As shown, the distribution has local minimums  1120 ,  1130 ,  1140 ,  1160  about 45°, 135°, 225°, and 315°. Accordingly, the angle space  1170  is shown to be partitioned by those ranges to form the angle bins. 
       FIGS. 12A and 12B  are diagrams illustrating an example of processing image data to generate geometric descriptor data, according to some example embodiments.  FIG. 12A  shows an original image.  FIG. 12B  shows detected SURF descriptor vectors with each line representing the angle of the keypoint. The lines are shaded by the membership of the angle. Angles are grouped into four groups using Von Mises model. The image area  1230  primarily includes descriptor vectors of a first angle bin. The image area  1240  primarily includes descriptor vectors of a second angle bin. The image area  1250  primarily includes descriptor vectors of a third angle bin. The image area  1260  primarily includes descriptor vectors of a fourth angle bin. The grouping of the angular information reveals information about the image structure and can be used to match images. 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium) or hardware modules. A “hardware module” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. 
     In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein. 
     In some embodiments, a hardware module may be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module may be a special-purpose processor, such as a Field-Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC). A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware modules become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the phrase “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where a hardware module comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware modules) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module implemented using one or more processors. 
     Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an Application Program Interface (API)). 
     The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors or processor-implemented modules may be distributed across a number of geographic locations. 
     The modules, methods, applications and so forth described in conjunction with  FIGS. 2-12  are implemented in some embodiments in the context of a machine and an associated software architecture. The sections below describe representative software architecture(s) and machine (e.g., hardware) architecture that are suitable for use with the disclosed embodiments. 
     Software architectures are used in conjunction with hardware architectures to create devices and machines tailored to particular purposes. For example, a particular hardware architecture coupled with a particular software architecture will create a mobile device, such as a mobile phone, tablet device, or so forth. A slightly different hardware and software architecture may yield a smart device for use in the “internet of things,” while yet another combination produces a server computer for use within a cloud computing architecture. Not all combinations of such software and hardware architectures are presented here as those of skill in the art can readily understand how to implement the inventive subject matter in different contexts from the disclosure contained herein. 
       FIG. 13  is a block diagram  1300  illustrating a representative software architecture  1302 , which may be used in conjunction with various hardware architectures herein described.  FIG. 13  is merely a non-limiting example of a software architecture and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture  1302  may be executing on hardware such as machine  1400  of  FIG. 14  that includes, among other things, processors  1410 , memory/storage  1430 , and I/O components  1450 . A representative hardware layer  1304  is illustrated and can represent, for example, the machine  1400  of  FIG. 14 . The representative hardware layer  1304  comprises one or more processing units  1306  having associated executable instructions  1308 . Executable instructions  1308  represent the executable instructions of the software architecture  1302 , including implementation of the methods, modules and so forth of  FIGS. 2-11 , as described below. Hardware layer  1304  also includes memory and/or storage modules  1310 , which also have executable instructions  1308 . Hardware layer  1304  may also comprise other hardware as indicated by  1312  that represents any other hardware of the hardware layer  1304 , such as the other hardware illustrated as part of machine  1400 . 
     In the example of  FIG. 13 , the software architecture  1302  may be conceptualized as a stack of layers where each layer provides particular functionality. For example, the software architecture  1302  may include layers such as an operating system  1314 , libraries  1316 , frameworks/middleware  1318 , applications  1320  and presentation layer  1344 . Operationally, the applications  1320  and/or other components within the layers may invoke application programming interface (API) calls  1324  through the software stack and receive a response, returned values, and so forth illustrated as messages  1326  in response to the API calls  1324 . The layers illustrated are representative in nature; not all software architectures have all layers. For example, some mobile or special purpose operating systems may not provide a frameworks/middleware  1318 , while others may provide such a layer. Other software architectures may include additional or different layers. 
     The operating system  1314  may manage hardware resources and provide common services. The operating system  1314  may include, for example, a kernel  1328 , services  1330 , and drivers  1332 . The kernel  1328  may act as an abstraction layer between the hardware and the other software layers. For example, the kernel  1328  may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services  1330  may provide other common services for the other software layers. The drivers  1332  may be responsible for controlling or interfacing with the underlying hardware. For instance, the drivers  1332  may include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth depending on the hardware configuration. 
     The libraries  1316  may provide a common infrastructure that may be utilized by the applications  1320  and/or other components and/or layers. The libraries  1316  typically provide functionality that allows other software modules to perform tasks in an easier fashion than to interface directly with the underlying operating system  1314  functionality (e.g., kernel  1328 , services  1330  and/or drivers  1332 ). The libraries  1316  may include system libraries  1334  (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries  1316  may include API libraries  1336  such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D in a graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries  1316  may also include a wide variety of other libraries  1338  to provide many other APIs to the applications  1320  and other software components/modules. 
     The frameworks/middleware  1318  (also sometimes referred to as middleware) may provide a higher-level common infrastructure that may be utilized by the applications  1320  and/or other software components/modules. For example, the frameworks/middleware  1318  may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks/middleware  1318  may provide a broad spectrum of other APIs that may be utilized by the applications  1320  and/or other software components/modules, some of which may be specific to a particular operating system or platform. 
     The applications  1320  include built-in applications  1340 , third party applications  1342 , and/or an image retrieval application  1343 . Examples of representative built-in applications  1340  may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and/or a game application. Third party applications  1342  may include any of the built-in applications  1340  as well as a broad assortment of other applications. In a specific example, the third party application  1342  (e.g., an application developed using the Android™ or iOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as iOS™, Android™, Windows® Phone, or other mobile operating systems. In this example, the third party application  1342  may invoke the API calls  1324  provided by the mobile operating system such as operating system  1314  to facilitate functionality described herein. The image retrieval application  1343  can include executable instructions of the implementation of the methods, modules, and so forth of  FIGS. 2-12 . In this example, the image retrieval application  1343  invokes the API calls  1324  provided by the mobile operating system, such as operating system  1314 , to facilitate functionality described in connection with  FIGS. 2-12 . 
     The applications  1320  may utilize built in operating system functions (e.g., kernel  1328 , services  1330  and/or drivers  1332 ), libraries (e.g., system libraries  1334 , API libraries  1336 , and other libraries  1338 ), and frameworks/middleware  1318  to create user interfaces to interact with users of the software architecture  1302 . Alternatively, or additionally, in some systems, interactions with a user may occur through a presentation layer, such as presentation layer  1344 . In these systems, the application/module “logic” can be separated from the aspects of the application/module that interact with a user. 
     As stated, in the illustrated example embodiment, the applications  1320  deploy the modules  210 - 250  of the image retrieval system. It will be appreciated, however, that the modules  210 - 250  of the image retrieval system  150  can be implemented in one or more of the operating system  1314 , the libraries  1316 , the frameworks/middleware  138 , the applications  1320 , and the presentation layer  1344  in alternative example embodiments. Additionally or alternatively, additional or fewer layers can exist in alternative embodiments. For example, in a mobile device (e.g., user device  110 ), fewer layers may exist (for example, the frameworks/middleware layer  1318  may not exist), so for mobile devices at least portions of the modules  210 - 230  illustrated in  FIG. 2  can be implemented as the image retrieval application  1343  in conjunction with libraries and operating system services. In a server device (e.g., application server  140 ), additional layers may exist (for example, networking, security, encryption, and/or virtualization layers may exist), so for server devices at least portions of the modules  210 - 250  illustrated in  FIG. 2  can be implemented as the image retrieval application  1343  in conjunction with these additional layers as well as the middleware, libraries, and operating system services. 
     Some software architectures utilize virtual machines. In the example of  FIG. 13 , this is illustrated by virtual machine  1348 . A virtual machine creates a software environment where applications/modules can execute as if they were executing on a hardware machine (such as the machine of  FIG. 14 , for example). A virtual machine is hosted by a host operating system (operating system  1314  in  FIG. 14 ) and typically, although not always, has a virtual machine monitor  1346 , which manages the operation of the virtual machine  1348  as well as the interface with the host operating system (i.e., operating system  1314 ). The software architecture  1302  executes within the virtual machine such as an operating system  1350 , libraries  1352 , frameworks/middleware  1354 , applications  1356  and/or presentation layer  1358 . These layers of software architecture  1302  executing within the virtual machine  1348  can be the same as corresponding layers previously described or may be different. 
       FIG. 14  is a block diagram illustrating components of a machine  1400 , according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,  FIG. 14  shows a diagrammatic representation of the machine  1400  in the example form of a computer system, within which instructions  1416  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1400  to perform any one or more of the methodologies discussed herein may be executed. For example the instructions  1416  may cause the machine  1400  to execute the flow diagrams of  FIGS. 8-11 . Additionally, or alternatively, the instructions  1416  may implement the modules  210 - 250  of  FIG. 2 . The instructions  1416  transform the general, non-programmed machine into a particular machine programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine  1400  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1400  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  1400  may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), or any machine capable of executing the instructions  1416 , sequentially or otherwise, that specify actions to be taken by machine  1400 . Further, while only a single machine  1400  is illustrated, the term “machine” shall also be taken to include a collection of machines  1400  that individually or jointly execute the instructions  1416  to perform any one or more of the methodologies discussed herein. 
     The machine  1400  may include processors  1410 , memory/storage  1430 , and I/O components  1450 , which may be configured to communicate with each other such as via a bus  1402 . In an example embodiment, the processors  1410  (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, processor  1412  and processor  1414  that may execute instructions  1416 . The term “processor” is intended to include multi-core processor that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although  FIG. 14  shows multiple processors  1410 , the machine  1400  may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core process), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof. 
     The memory/storage  1430  may include a memory  1432 , such as a main memory or other memory storage, and a storage unit  1436 , both accessible to the processors  1410  such as via the bus  1402 . The storage unit  1436  and memory  1432  store the instructions  1416  embodying any one or more of the methodologies or functions described herein. The instructions  1416  may also reside, completely or partially, within the memory  1432 , within the storage unit  1436 , within at least one of the processors  1410  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1400 . Accordingly, the memory  1432 , the storage unit  1436 , and the memory of processors  1410  are examples of machine-readable media. 
     As used herein, “machine-readable medium” means a device able to store instructions and data temporarily or permanently and may include, but is not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions  1416 . The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions  1416 ) for execution by a machine (e.g., machine  1400 ), such that the instructions, when executed by one or more processors of the machine  1400  (e.g., processors  1410 ), cause the machine  1400  to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” excludes signals per se. 
     The I/O components  1450  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  1450  that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  1450  may include many other components that are not shown in  FIG. 14 . The I/O components  1450  are grouped according to functionality merely for simplifying the following discussion, and the grouping is in no way limiting. In various example embodiments, the I/O components  1450  may include output components  1452  and input components  1454 . The output components  1452  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components  1454  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O components  1450  may include biometric components  1456 , motion components  1458 , environmental components  1460 , or position components  1462  among a wide array of other components. For example, the biometric components  1456  may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification), and the like. The motion components  1458  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  1460  may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometer that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  1462  may include location sensor components (e.g., a Global Position System (GPS) receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  1450  may include communication components  1464  operable to couple the machine  1400  to a network  1480  or devices  1470  via coupling  1482  and coupling  1472  respectively. For example, the communication components  1464  may include a network interface component or other suitable device to interface with the network  1480 . In further examples, communication components  1464  may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices  1470  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a Universal Serial Bus (USB)). 
     Moreover, the communication components  1464  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1464  may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph. MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  1464 , such as location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting a NFC beacon signal that may indicate a particular location, and so forth. 
     In various example embodiments, one or more portions of the network  1480  may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network  1480  or a portion of the network  1480  may include a wireless or cellular network and the coupling  1482  may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other type of cellular or wireless coupling. In this example, the coupling  1482  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard setting organizations, other long range protocols, or other data transfer technology. 
     The instructions  1416  may be transmitted or received over the network  1480  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  1464 ) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  1416  may be transmitted or received using a transmission medium via the coupling  1472  (e.g., a peer-to-peer coupling) to devices  1470 . The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions  1416  for execution by the machine  1400 , and includes digital or analog communications signals or other intangible medium to facilitate communication of such software. 
     Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     Although an overview of the inventive subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or inventive concept if more than one is, in fact, disclosed. 
     The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.