Abstract:
One embodiment is a method for selecting and grouping key points extracted by applying a feature detector on a scene being analyzed. The method includes grouping the extracted key points into clusters that enforce a geometric relation between members of a cluster, scoring and sorting the clusters, identifying and discarding clusters that are comprised of points which represent the background noise of the image, and sub-sampling the remaining clusters to provide a smaller number of key points for the scene.

Description:
PRIORITY CLAIM 
     This application claims priority to U.S. Provisional Patent Application Nos. 61/596,111, 61/596,149, and 61/596,142, all filed on Feb. 7, 2012 and each of which is incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     Embodiments of the present disclosure relate generally to visual search systems and, more specifically to systems, circuits, and methods that group image feature descriptors of a captured scene into clusters to improve matching with reference images and the efficiency of transmission of such image feature descriptors. 
     BACKGROUND 
     Visual search systems are known and operate to use captured images as “queries” for a database of reference images in order to retrieve information related to the content of the captured image. For example, after taking a photo of the facade of a museum, a user&#39;s smartphone that was used to capture the image processes the image to generate feature descriptors that effectively describe the image for purposes of the query. In such a situation, which is a rapidly growing area of visual search research and development, the smartphone thereafter communicates the generated feature descriptors to a remote system containing a database, and searches or queries the database using the feature descriptors to identify the captured image in the database. The remote system may thereafter communicate to the smartphone the results of this query for presentation to the user, such as the location, opening times, and ticket costs where the captured image is a museum that the user is interested in visiting. 
     A typical visual search pipeline includes an interest-points detector, a features descriptor generator, a matching stage, and a geometry consistency checker. The most successful visual search techniques make use of invariant features, where the term invariant refers to the ability of the detection algorithm to detect image points and describe its surrounding region in order to be tolerant to affine transformation, like rotation, translation and scaling. In the current state of the art there exist many invariant feature extraction and description algorithms. The Scale Invariant Feature Transform (SIFT) algorithm is often used as a reference algorithm because it is typically the algorithm that provides the best recognition rate (i.e., matching of feature descriptors associated with a captured image with the proper corresponding image in an image database). The computational costs of the SIFT algorithm, however, are quite high (e.g., less than 2 frames per second (FPS) at VGA resolution on a modern desktop computer) so there are other algorithms, like the Speeded Up Robust Features (SURF) algorithm, that sacrifice precision as obtained with the SIFT algorithm for improved speed. 
     In the typical visual search pipeline, the matching stage simply consists of a many to many comparison between the feature descriptors in the scene and the ones stored in the database using a predefined metric function which is dependent on the description algorithm (e.g. ratio of L2 norms for SIFT feature descriptors). Finally, the geometry consistency checker, such as a Random Sample Consensus (RANSAC) checker, processes the entire set of matched feature descriptors to retrieve a valid transformation model for the query and reference images with the goal of removing false matches (i.e., outliers) and increasing the recognition quality of the algorithm. Typical visual search applications utilize pair-wise matching, which simply compares two images, and a more complex analysis named retrieval in which a query image is looked up inside of a reference image data set that is potentially a very large image database like Google Images™ or Flickr™. 
     Each of the phases described above requires high computational costs due to the amount of data involved or the complexity of the calculations. Also the number of bytes (i.e., the length) used in the feature descriptor is an important factor for a potential transmission overhead in a client-server environment, such as where a mobile device like a smartphone (i.e., the client) is the image capture device, as well as for amount of storage space required to store all the desired the content of the image database (i.e., the server). Improvements in the matching stage have been proposed, such as by the creator of the SIFT algorithm who proposed the use of KD-Trees to approximate the lookup of the nearest feature vector inside a database. Other improvements have been applied to the geometry consistency checker using algorithms that are less computationally intensive than RANSAC, such as the DISTRAT algorithm. Finally the compression and transmission of the image&#39;s features in the form of the feature descriptors is the core topic of Motion Pictures Expert Groups (MPEG) standardization group named Compact Descriptors for Visual Search (CDVS). For example, the Compress Histogram of Gradients (CHOG) algorithm is a feature descriptor algorithm or approach designed to produce a compact representation by applying sampling and discretization to SIFT-like feature descriptors. 
     SUMMARY 
     An embodiment is a method for selecting and grouping key points extracted by applying a feature detector on a scene being analyzed. The method includes grouping the extracted key points into clusters that enforce a geometric relation between members of a cluster, scoring and sorting the clusters, identifying and discarding clusters that are comprised of points which represent the background noise of the image, and sub-sampling the remaining clusters to provide a smaller number of key points for the scene. The method of can include iteratively applying the operations of grouping through sub-sampling to a set of reference images including sets comprised of a single reference image. The method can further include using clusters information to match clusters in the two images by using a metric which is defined by selected invariant features descriptors and by exploiting the sub-sampled cluster data to reduce the complexity of the matching phase including an early discard of clusters that cannot be probably found between extracted clusters. The method also can include tracking multiple independently moving objects in the scene being analyzed or detecting multiple objects found in the scene being analyzed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram of visual search system including search pipelines of  FIG. 2  or  3  according to one embodiment of the present disclosure. 
         FIG. 2  is a functional block diagram illustrating a sequential visual search pipeline according to one embodiment described in the present disclosure. 
         FIG. 3  is a functional block diagram illustrating a parallel visual search pipeline according to another embodiment described in the present disclosure. 
         FIG. 4  is a functional block diagram of the clustering module of  FIGS. 3 and 4  according to one embodiment described in the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Visual search for mobile devices relies on transmitting wirelessly a compact representation of the captured or query image, generally in the form of feature descriptors, to a remote server. The feature descriptors are therefore compressed so as to reduce the bandwidth occupancy and network latency of this transmission. Given the impressive pace of growth of 3D video technology, 3D visual search applications for the mobile and the robotic markets will become a reality. Accordingly, embodiments described herein are directed to improving detection of salient features and generation of corresponding feature descriptors that reduce the bandwidth required for such prospective applications through a hierarchical method using clustering of invariant features of a scene being analyzed, as will be described in more detail below. 
     A representative visual search system  100  is illustrated in  FIG. 1  and includes a local image capture device  102 , such as a mobile device like a smartphone, an automobile  103  including two-dimensional or three-dimensional sensors for navigation, or an image capture system positioned at a certain location in a city such as a camera (not shown in  FIG. 1 ) mounted on top of a lamppost at a particular intersection. The local image capture device  102  generates two- or three-dimensional uncompressed feature descriptors for the scene being imaged. These feature descriptors must then be communicated over a communications network  104  to a remote server  106  containing a visual database  108  that will be queried to identify an image in the database corresponding to the local image captured by the device  102  and represented by the communicated feature descriptors. The remote server  106  then returns the visual search results to the local image capture device  102  for use by the device or a user of the device. Embodiments disclosed herein are directed to methods of compressing these uncompressed feature descriptors generated by the local image capture device  102  so that the bandwidth of the communications network  104  is sufficient to provide the desired overall operation of the system  100 . As illustrated in  FIG. 1 , the device  102  provides compressed 3D feature descriptors in this example over the communication network  104  to the remote server  106 . 
     In the following description, certain details are set forth to provide a sufficient understanding of the present invention, but one skilled in the art will appreciate that the invention may be practiced without these particular details. Furthermore, one skilled in the art will appreciate that the example embodiments described below do not limit the scope of the present invention, and will also understand various modifications, equivalents, and combinations of the disclosed example embodiments and components of such embodiments are within the scope of the present invention. Illustrations of the various embodiments, when presented by way of illustrative examples, are intended only to further illustrate certain details of the various embodiments, and should not be interpreted as limiting the scope of the present invention. Finally, in other instances below, the operation of well known components, processes, algorithms and protocols have not been shown or described in detail to avoid unnecessarily obscuring the present invention. 
     State of art invariant feature extraction and feature descriptor generation algorithms initially produce a large set of features and feature descriptors. After the matching and geometry checking phases, the number of features that could be correctly paired is an order of magnitude less. Because the matching stage compares each feature descriptor in a query image A with all the feature descriptors in a reference image B in an image database, the matching stage wastes a lot of time computing useless correspondences. In the literature some improvement are presented to improve comparison time performance by applying, for example, approximated research strategies based on search trees. 
     A query image does not usually contain only the subject of the research but also other objects and a background environment that results in the generation of feature descriptors that introduce noise in the query set. Embodiments described herein group the identified interest points in an image by evaluating a geometric constraint between them in order to later apply a full search comparison to a smaller extracted features data set, and the corresponding feature descriptors, which are geometrically similar. Embodiments also automatically identify and remove noisy features in the query image and thus send the smallest number possible of feature descriptors to the matching phase (i.e., from the local image capture device  102  to the remote server  106  in  FIG. 1 ). Embodiments also detect multiple objects in a scene being analyzed. 
     The first step in embodiments described herein, after key point detection or feature extraction to generate corresponding feature descriptors, is a clustering stage that groups the points or feature descriptors using a desired metric. This is seen in  FIG. 2  which is a functional block diagram illustrating a visual search pipeline  200  according to one embodiment described in the present disclosure. In one embodiment, the metric or algorithm exploits the spatial coherence of the key points or feature descriptors, as will be described in more detail below. In  FIG. 2 , the operations, modules, components, or circuits contained between the vertical dotted lines correspond to correspond to operations or circuitry in a local image capture device  202  (i.e., corresponds to device  102  of  FIG. 1 ) while those to the right of the rightmost vertical dotted line correspond to those operations or circuitry in the server  106  of  FIG. 1 . 
     In the pipeline  200 , an image is captured through an image capture device  202  and a key points detector  204  then identifies or extracts key points in the image and supplies these key points to a clustering module  206 . The clustering module  206  then performs a grouping or clustering process on the extracted key points, or the corresponding feature descriptors, to generate clusters of features or feature descriptors. In one embodiment, the clustering process or metric executed by the clustering module  204  computes the distance of each key point or feature descriptor from its neighboring key points or feature descriptors in applying an eight-distance methodology in a prefixed circular region around the key point or feature descriptor being processed. If memory is available and a fixed image size is being used, this step can be replaced with a look-up table to save computational cost. The clustering module  204  executed the clustering metric to keep creating and/or merging clusters following a proximity relation of the clusters. 
     The module  204  executes this metric or algorithm until the size and density of the clusters reach a variable threshold. This process builds a variable number of clusters with different densities that we can associate to the idea of the quantity of information of the area. In accordance with this interpretation it is then possible to remove the low density sets or the smallest ones in order to focus the computation only on the portion of the image which is rich in information (i.e., contains a lot of feature descriptors). Accordingly, a subsampling module  206  selects, from among the clusters generated by the module  204 , a number of the clusters identified as including more likely to include key points for further processing. A compression module  210  then compresses these features or key points in the selected clusters and communicates these compressed features to the server, which corresponds to the components to the right of the rightmost vertical dashed line in  FIG. 2  as mentioned above. 
     A reduced matching module  212  on the server side then executes a reduced database matching process to identify potential images stored in a reference image database  214  that may correspond to the captured image. A return module  216  then returns matched clusters, which are identified in the figure as “matching scene clusters,” to the local image capture device. A cluster selection module  218  receives the matching scene clusters and provides them to a matched cluster descriptor module  220  which then generates the feature descriptors only for the features or key points contained in these matched clusters. A compression module  222  then compresses the generated feature descriptors and communicates these compressed feature descriptors to the server for further processing. More specifically, in the server a database data selection module  224  selects reference images from the database  214  to be further searched based on the matched cluster data from the return module  216  and corresponding images from the database  214 . The database data selection module  224  provides this data to a return module  226  in the server, which also receives the compressed feature descriptors from the module  222 . The server then performs additional visual searching through a features matching module  228  that uses the compressed feature descriptors and the matched cluster data and corresponding images to thereby identify reference images in the database  214  that correspond to the image captured by the device  202 . 
     A geometric verification module  230  then analyzes the results of the most similar search results to ensure that these identified possible matches of the captured query image to the identified reference images in the database  214  are plausible. The module  230  processes the entire set of matched features between pairs of the query and identified references images to identify a valid transformation model between the two images in each pair in order to remove false reference image matches (i.e., eliminate outliers) and thereby increase the recognition quality of the pipeline  200 . After processing by the geometric verification module  230 , the module returns the identified reference image or image, typically along with additional information about these images, to the local image capture device  202  for suitable display and use on that device. The approach implemented through the visual search pipeline  200  of  FIG. 2  removes background and low density information regions of captured image that hardly produce a correct association at the end of the entire process. This approach also allows ranking between several features clusters that can be exploited by the matching strategy being implemented, and potentially allows the search algorithm implemented in the server to work only on the most significant image region. 
       FIG. 3  is a functional block diagram illustrating a parallel visual search pipeline  300  according to another embodiment of the present disclosure. The pipeline  300  includes the circuits, module, components or operations  302 - 332  whose functionality and operation will be clear from the detailed discussion above of corresponding or similar components  202 - 230  in the pipeline  200  of  FIG. 2  and thus, for the sake of brevity, the detailed operation of these circuits or operations  302 - 332  will not be described in detail herein. The configuration shown in the embodiment of  FIG. 3  is a parallel pipeline configuration as compared to the embodiment of the pipeline  200  just described with reference to  FIG. 2 , which is a sequential implementation. This is true because in the pipeline  300  the pipeline starts the computation of the feature descriptors of the remaining sets of clusters in a remaining clusters descriptors module  314  while the matching stage including the modules  316 - 320  in the server are still processing the quick matches provided from module  310 . This approach may result in an increase in power consumption of the local image capture device  302  due to the possible wasted computations. This approach may nonetheless be useful to increase the overall speed of the pipeline  300 , particularly where the bandwidth of the network coupling the local image capture device  302  to the server, and where the size of the reference image database  318  is very large and thus the search will take longer. 
       FIG. 4  is a functional block diagram of the clustering module  206 / 306  of  FIGS. 3 and 4  according to one embodiment described in the present disclosure. The functionality of the operations or circuits  402 - 410  will be understood in view of the discussion of  FIGS. 2 and 3  set forth above and thus, for the sake of brevity, will not be described in more detail herein. 
     Normally the reference image portrays the object without ambiguity, so the top left edge of the object lie exactly in the same position in the image. This assumption is not true for the query image, because the object can appear in any position or rotation in the captured image, however, preventing the trivial match of the top left region of the two images. The proposed solutions in the described embodiments may randomly select, in each features cluster of both the query and reference images, a number of evenly spread candidates and may perform a full matching on this candidate data. This procedure allows the removal of reference system incoherence and quickly discriminating different objects using only a restricted number of features. In some embodiments, objects in captured image may be successfully identified using only 20% of the initial data for the captured image. 
     An issue in visual search system may be introduced by possible differences of scale of an object between the reference image and the query image. The increment of information in the magnified image, in fact, can generate multiple clusters that have to be potentially associated with a single cluster in the other image. To avoid this incoherence, embodiments of the proposed pipelines merge clusters that are part of a many-to-one relation. 
     Finally, in one embodiment to refine this quick discrimination and obtain an unique and robust result, the matching stage requests the transmission of the features belonging to the survived sets of clusters and after a new matching process, using all available data, removes most of the outliers using a RANSAC checking algorithm strategy to each cluster association. 
     The processes explained in the embodiments above allow multiple configurations of the pipeline  200 / 300  that have to be chosen with respect to the target application. the first possible configuration, namely the sequential pipeline, is shown in  FIG. 2  and waits for the quick matching response and then computes the feature descriptors only in the survived sets potentially saving a lot of computation, particularly in the case of a non-matching query image, as discussed above. This leads to a lower power consumption of the local image capture device  202  since the processor (not shown in  FIG. 2 ) performing these operations can remain idle during the matching stage being performed by the server. 
     Since the disclosed algorithms apply the geometry consistency check on the clusters pairs instead of the whole matching descriptors of the scene, embodiments according to the disclosed approach allow the recognition of objects belonging to two different scenes when no real affine transformation exists between the scenes themselves (e.g. the system can track two objects that are moving independently one from the other). 
     One skilled in the art will understand that even though various embodiments and advantages of the present disclosure have been set forth in the foregoing description, the above disclosure is illustrative only, and changes may be made in detail, and yet remain within the broad principles of the disclosure. For example, many of the components described above may be implemented using either digital or analog circuitry, or a combination of both, and also, where appropriate, may be realized through software executing on suitable processing circuitry. It should also be noted that the functions performed can be combined to be performed by fewer elements or process steps depending upon the actual embodiment being used in the system  100  of  FIG. 1 . Therefore, the present disclosure is to be limited only by the appended claims.