Patent Publication Number: US-2023141005-A1

Title: Image processing apparatus and method

Description:
TECHNICAL FIELD 
     The present disclosure relates to an image processing apparatus and method, and, more particularly, to an image processing apparatus and method capable of suppressing an increase in a processing time for image clustering. 
     BACKGROUND ART 
     Conventionally, image clustering has been used for various image processing (see, for example, Patent Document 1). For example, Patent Document 1 discloses a method of clustering an image, interpolating pixels by using class data of the image, and restoring thinned pixels. 
     CITATION LIST 
     Patent Document 
     
         
         Patent Document 1: Japanese Patent Application Laid-Open No. 5-328185 
       
    
     SUMMARY OF THE INVENTION 
     Problems to be Solved by the Invention 
     However, according to clustering of a conventional method, all pixels of a processing target image are clustered, and therefore a processing time is concerned to increase. 
     The present disclosure has been made in view of such a situation, and makes it possible to suppress an increase in a processing time of image clustering. 
     Solutions to Problems 
     An image processing apparatus according to one aspect of the present technology is an image processing apparatus that includes: a clustering unit configured to cluster a sparse pixel included in an image; and an interpolation processing unit configured to interpolate sparse information by image filtering, and thereby derive a dense clustering result, the sparse information being obtained by the clustering of the clustering unit, and the image filtering using an image signal as a guide. 
     An image processing method according to one aspect of the present technology is an image processing method that includes: clustering a sparse pixel included in an image; and interpolating sparse information by image filtering, and thereby deriving a dense clustering result, the sparse information being obtained by the clustering, and the image filtering using an image signal as a guide. 
     An image processing apparatus according to another aspect of the present technology is an image processing apparatus that includes a clustering unit configured to perform local clustering by using information, the local clustering being clustering of a dense pixel included in a local area of an image, and the information being obtained by wide area clustering that is clustering of a sparse pixel included in a wide area of the image. 
     An image processing method according to another aspect of the present technology is an image processing method that includes performing local clustering by using information, the local clustering being clustering of a dense pixel included in a local area of an image, and the information being obtained by wide area clustering that is clustering of a sparse pixel included in a wide area of the image. 
     The image processing apparatus and method according to the one aspect of the present technology cluster sparse pixels included in an image, interpolate sparse information obtained by this clustering by image filtering that uses an image signal as a guide, and thereby derive a dense clustering result. 
     The image processing apparatus and method according to the another aspect of the present technology perform local clustering that is clustering of dense pixels included in a local area of an image, by using information obtained by wide area clustering that is clustering of sparse pixels included in a wide area of the image. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a block diagram illustrating a main configuration example of an image processing apparatus. 
         FIG.  2    is a view for explaining an example of how image filtering is performed. 
         FIG.  3    is a view for explaining an example of sparse model coefficients. 
         FIG.  4    is a view for explaining an example of a guide. 
         FIG.  5    is a view for explaining an example of dense model coefficients. 
         FIG.  6    is a view for explaining an example of a clustering result. 
         FIG.  7    is a flowchart for explaining an example of a flow of clustering processing. 
         FIG.  8    is a block diagram illustrating a main configuration example of an image processing apparatus. 
         FIG.  9    is a view for explaining an example of a field. 
         FIG.  10    is a flowchart for explaining an example of a flow of clustering processing. 
         FIG.  11    is a block diagram illustrating a main configuration example of an image processing apparatus. 
         FIG.  12    is a view for explaining an example of stitching information. 
         FIG.  13    is a flowchart for explaining an example of a flow of clustering processing. 
         FIG.  14    is a block diagram illustrating a main configuration example of an image processing apparatus. 
         FIG.  15    is a flowchart for explaining an example of a flow of clustering processing. 
         FIG.  16    is a view for explaining an example of an outline of image clustering. 
         FIG.  17    is a block diagram illustrating a main configuration example of an image processing apparatus. 
         FIG.  18    is a flowchart for explaining an example of a flow of clustering processing. 
         FIG.  19    is a block diagram illustrating a main configuration example of an image processing apparatus. 
         FIG.  20    is a flowchart for explaining an example of a flow of clustering processing. 
         FIG.  21    is a block diagram illustrating a main configuration example of an image processing apparatus. 
         FIG.  22    is a view for explaining an example of how clustering results are compared. 
         FIG.  23    is a flowchart for explaining an example of a flow of clustering processing. 
         FIG.  24    is a block diagram illustrating a main configuration example of an image processing apparatus. 
         FIG.  25    is a flowchart for explaining an example of a flow of clustering processing. 
         FIG.  26    is a block diagram illustrating a main configuration example of an image processing apparatus. 
         FIG.  27    is a flowchart for explaining an example of a flow of clustering processing. 
         FIG.  28    is a view for explaining an example of how CT images are generated. 
         FIG.  29    is a view for explaining an example of how CT images illustrating an example of a global area and a local area are generated. 
         FIG.  30    is a block diagram illustrating a main configuration example of an image processing apparatus. 
         FIG.  31    is a flowchart for explaining an example of a flow of clustering processing. 
         FIG.  32    is a block diagram illustrating a main configuration example of a computer. 
     
    
    
     MODE FOR CARRYING OUT THE INVENTION 
     Hereinafter, modes for carrying out the present disclosure (hereinafter referred to as embodiments) will be described. Note that the description will be given in the following order. 
     1. First Embodiment (Sparse Clustering and Image Filtering) 
     2. Second Embodiment (Wide Area Clustering and Sparse Local Clustering) 
     3. Third Embodiment (Wide Area Clustering and Dense Local Clustering) 
     4. Fourth embodiment (Clustering in Vegetation Area Analysis) 
     5. Fifth embodiment (Clustering of CT Images) 
     6. Supplementary Note 
     1. First Embodiment 
     &lt;Image Clustering&gt; 
     Conventionally, image clustering has been used for various image processing. For example, Patent Document 1 discloses a method of clustering an image, interpolating pixels by using class data of the image, and restoring thinned pixels. 
     Furthermore, in a case where, for example, a so-called drone, an airplane, or the like images a field a plurality of times from the sky while moving, and vegetation is analyzed (vegetation and soil are classified or the like) by using this captured image, image clustering is used. 
     However, according to clustering of the conventional method, all pixels of a processing target image are clustered, and therefore a processing time is concerned to increase. 
     &lt;Sparse Clustering and Image Filtering&gt; 
     Hence, sparse pixels included in an image are clustered, sparse information obtained by this clustering is interpolated by image filtering that uses an image signal as a guide, and thereby a dense clustering result is derived. The information for which this image filtering is performed may be, for example, model coefficients of learning, a clustering result, or the like. “Interpolation” by this image filtering means not only interpolation of information (filling of missing data), but also optimization or the like according to an image structure as appropriate. That is, an optimized dense clustering result is obtained by this image filtering. 
     In a case of, for example, a captured image of a field, imaging is performed in an outdoor environment, and therefore there is a probability that lighting environment changes significantly during an imaging work, and a cast shadow, a shading, or the like causes unevenness in a signal distribution in the same subject (a plurality of pixels of the same subject has different signal characteristics). Even in such a case, by performing clustering as described above, it is possible to obtain a clustering result that uses image structure information of surroundings at a high speed. That is, by applying the present technology, it is possible to reflect, in the clustering result, regularization that matches a geometric structure of a guide image, so that it is possible to obtain a result classified per subject even from an image showing a significant change in lighting environment outdoors or an image having unevenness in a signal distribution in the same subject due to a cast shadow or a shading. 
     &lt;Image Processing Apparatus&gt; 
       FIG.  1    is a block diagram illustrating an example of a configuration of an image processing apparatus to which the present technology is applied. An image processing apparatus  100  illustrated in  FIG.  1    is an apparatus that performs image clustering. The image processing apparatus  100  receives a captured image  20  as input, performs the image clustering on this captured image  20 , and outputs a clustering result  30  of this image clustering. 
     The captured image  20  may be, for example, a stitching image obtained by stitching a plurality of captured images (P 1  to Pn). Furthermore, the captured image  20  may be a moving image including a plurality of frame images. Furthermore, the captured image  20  may be a file (captured image group) obtained by integrating a plurality of captured images into one, or may be one captured image. Naturally, the captured image  20  may be an image other than a captured image (e.g., a CG image or the like). Furthermore, this captured image  20  may be an image of a wavelength range of visible light (RGB), or may be an image obtained by imaging a wavelength range of invisible light such as near-infrared light. Furthermore, the captured image  20  may be both of these images. 
     Note that  FIG.  1    illustrates main elements such as processing units and data flows, and the elements illustrated in  FIG.  1    are not necessarily all. That is, in this image processing apparatus  100 , there may be a processing unit that is not illustrated as a block in  FIG.  1   , or there may be processing or a data flow that is not illustrated as an arrow or the like in  FIG.  1   . 
     As illustrated in  FIG.  1   , the image processing apparatus  100  includes a sampling pixel selection unit  111 , a clustering unit  112 , and an interpolation processing unit  113 . 
     The sampling pixel selection unit  111  performs processing related to selection of sampling pixels that are clustering target pixels. For example, the sampling pixel selection unit  111  obtains the captured image  20 . Furthermore, the sampling pixel selection unit  111  selects part of pixels of this captured image  20  as sampling pixels. In this case, the sampling pixel selection unit  111  selects the sampling pixels such that the sampling pixels are in a sparse state. 
     The “sparse state” refers to a state of a pixel group (or information corresponding to this pixel group) including part of pixels of a captured image, and refers to at least a state of a pixel group (or information corresponding to this pixel group) including a smaller number of pixels than that of a “dense state” described later. For example, a pixel group (or information corresponding to this pixel group) including pixels having a positional relationship that the pixels are not adjacent to each other may be in the “sparse state”. That is, in a case of sampling pixels, the sampling pixels selected from only pixels having the positional relationship that the pixels are not adjacent to each other in the captured image  20  may be sampling pixels in the sparse state (also referred to as sparse sampling pixels). Furthermore, a pixel group (or information corresponding to this pixel group) selected from a predetermined image at a rate (number) smaller than a predetermined threshold may be in the “sparse state”. That is, in a case of sampling pixels, sampling pixels selected at the rate (number) smaller than the predetermined threshold with respect to the number of pixels of the captured image  20  may be the sparse sampling pixels. 
     The sampling pixel selection unit  111  supplies the selected sparse sampling pixels to the clustering unit  112 . 
     The clustering unit  112  performs processing related to clustering. For example, the clustering unit  112  obtains the sparse sampling pixels supplied from the sampling pixel selection unit  111 . The clustering unit  112  clusters these obtained sparse sampling pixels as processing targets. This clustering method is arbitrary. For example, a GMM, a k-means method, or the like may be applied. The clustering unit  112  supplies sparse information obtained by this clustering to the interpolation processing unit  113 . 
     This sparse information is information that is obtained by clustering the sparse sampling pixels, and corresponds to each sampling pixel (i.e., a sparse state). For example, the sparse information may be model coefficients of learning, may be a clustering result, or may be both of the model coefficients of learning and the clustering result. 
     The interpolation processing unit  113  performs processing related to interpolation of the sparse information. For example, the interpolation processing unit  113  obtains the sparse information (the model coefficients of learning, the clustering result, or the like) supplied from the clustering unit  112 . Furthermore, the interpolation processing unit  113  obtains the captured image  20 . 
     This captured image  20  may be the same as the captured image (i.e., the captured image to be clustered) supplied to the sampling pixel selection unit  111 , or may be a captured image whose time and range are substantially the same time and substantially the same range as those of the captured image to be clustered, and that is different from this captured image to be clustered. For example, the captured image  20  may be another captured image obtained by another imaging at substantially the same time and at substantially the same angle of view as imaging for obtaining the captured image to be clustered. For example, the captured image  20  of the wavelength range of visible light (RGB) may be supplied to the sampling pixel selection unit  111 , and the captured image  20  obtained by imaging the wavelength range of invisible light such as near-infrared ray may be supplied to the interpolation processing unit  113 . 
     The interpolation processing unit  113  performs image filtering (interpolation processing) on the sparse information obtained from the clustering unit  112  by using an image signal (obtained captured image  20 ) as a guide, and derives a clustering result of a dense state. 
     The “dense state” refers to a state of a pixel group (or information corresponding to this pixel group) including part or all of pixels of a captured image, and refers to at least a state of a pixel group (or information corresponding to this pixel group) including a larger number of pixels than that of the above-described “dense state”. For example, a pixel group (or information corresponding to this pixel group) including pixels, too, having a positional relationship that the pixels are adjacent to each other may be in the “dense state”. That is, in a case of a clustering result, the clustering result of the sampling pixels including pixels, too, having the positional relationship that the pixels are adjacent to each other in captured image  20  may be the dense state (also referred to as dense clustering result). Furthermore, a pixel group (or information corresponding to this pixel group) selected from a predetermined image at a rate (number) equal to or more than a predetermined threshold may be in the “dense state”. That is, in a case of a clustering result, the clustering result of sampling pixels selected at the rate (number) equal to or more than the predetermined threshold with respect to the number of pixels of the captured image  20  may be the dense clustering result. 
     For example, the interpolation processing unit  113  receives a likelihood (likelihood image) of each pixel for each class as an input, sequentially applies image filtering that uses the original image as a guide to perform interpolation, redetermines the class from this filtered likelihood image, and thereby acquires a dense clustering result. The image filtering can reflect, in the clustering result, regularization that matches a geometric structure of the guide image, so that the interpolation processing unit  113  can obtain a result classified per subject even from an image showing a significant change in lighting environment outdoors or an image showing unevenness in a signal distribution in the same subject due to a cast shadow or a shading. For example, it is possible to suppress occurrence of a phenomenon that part of a portion of the same color of the same subject becomes a shade and is classified into another class due to a difference in brightness. 
     The interpolation processing unit  113  outputs the clustering result  30  (dense clustering result) obtained by this interpolation processing as an image processing result of the image processing apparatus  100  to an outside of the image processing apparatus  100 . 
     &lt;Image Filtering&gt; 
     A method of this image filtering (interpolate processing) is arbitrary. By using edge-preserving filtering that operates at a high speed, such as Fast Global Smother filtering, Domain Transform filtering, Fast Bilateral Solver filtering, or Domain Transform Solver filtering as the image filtering, it is possible to obtain a clustering result that is robust against noise and disturbance influences at a higher speed than prediction in all pixels. 
     For example, the interpolation processing unit  113  may perform energy minimization of a clustering result by GrabCut disclosed in Jianbo Li, et. al, “KM_GrabCut: a fast interactive image segmentation algorithm”, ICGIP 2014. (also referred to as Non-Patent Document 1), perform wide area optimization by Cost-Volume Filtering disclosed in C. Rhemann, et. al, “Fast Cost-Volume Filtering for Visual Correspondence and Beyond”, CVPR 2011. (also referred to as Non-Patent Document 2), use an FGS filter disclosed in D. Min. et. al, “Fast Global Image Smoothing Based on Weighted Least Squares”, IEEE TIP 2014. (also referred to as Non-Patent Document 3), and highly densify information. 
     The fast global weighted least squares filter (FGWLS) disclosed in Non-Patent Document 3 is processing of decomposing a weighted least squares filter (WLS) disclosed in Z Farbman, et Al., “Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation,” Proceedings of ACM SIGGRAPH 2008. (also referred to as Non-Patent Document 4) into a one-dimensional recursive filter, repeatedly applying the one-dimensional recursive filter in x and y axis directions, and thereby obtaining an overall optimal solution by a constant time operation. By this processing, sparse data is expanded and highly densified according to an image structure of a texture, an edge or the like (according to an adjacent relationship between pixels obtained on the basis of this structure). 
     By using, for example, an image  130  including a gray and white spiral picture pattern as a guide as illustrated in A of  FIG.  2   , the above-described image filtering is performed on pixels in an area  131  of a first color indicated by a diagonal line pattern and pixels in an area  132  of a second color indicated by a mesh pattern. The area  131  of the first color is located in a gray area of the image  130 . The area  132  of the second color is located in a white area of the image  130 . 
     By repeatedly performing a linear recursive operation of adjacent pixels in the x and y directions, the area  131  of the first color is enlarged in the gray area of the image  130  as illustrated in B of  FIG.  2   , C of  FIG.  2   , and D of  FIG.  2   . Similarly, the area  132  of the second color is enlarged in the white area of the image  130 . Then, in the state in D of  FIG.  2   , an area on the image  130  is filled with the area  131  of the first color and the area  132  of the second color. That is, the area  131  of the first color and the area  132  of the second color that are in the sparse state in A of  FIG.  2    (that are sparse portions in the area on the image  130 ) are in the dense state in D of  FIG.  2    (a state where the areas on the image  130  are filled). 
     In this way, by performing the image filtering, it is possible to interpolate and highly densify sparse data according to a structure of an image used as a guide. Consequently, the image processing apparatus  100  can obtain a more accurate clustering result. Note that, as described above, “interpolation” by this filtering means not only interpolation of information (filling of missing data), but also optimization or the like according to an image structure as appropriate. That is, an optimized dense clustering result is obtained by this image filtering. Consequently, the image processing apparatus  100  can obtain a more accurate clustering result. 
     In addition to the above examples, as the image filtering, rule-based filtering disclosed in Eduardo S L Gastal and Manuel M Oliveira, “Domain transform for edge-aware image and video processing”, In ACM Transactions on Graphics (TOG), volume 30, page 69. ACM, 2011. (also referred to as Non-Patent Document 5), Jonathan T Barron and Ben Poole, “The Fast Bilateral Solver”, In European Conference on Computer Vision (ECCV), pages 617-632. Springer International Publishing, 2016. (also referred to as Non-Patent Document 6), and Akash Bapat, Jan-Michael Frahm, “The Domain Transform Solver”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6014-6023. (also referred to as Non-Patent Document 7), and the like may be applied. Furthermore, deep learning (Deep Neural Network (DNN))-based filtering disclosed in Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, Jan Kautz, “Pixel-Adaptive Convolutional Neural Networks”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (also referred to as Non-Patent Document 8), Yu-Kai Huang, Tsung-Han Wu, Yueh-Cheng Liu, Winston H. Hsu, “Indoor depth completion with Boundary Consistency and Self-Attention”, (ICCV), 2019. (also referred to as Non-Patent Document 9), and Jie Tang, Fei-Peng Tian, Wei Feng, Jian Li, Ping Tan, “Learning Guided Convolutional Network for Depth Completion”, arXiv preprint arXiv: 1908.01238, 2019. (also referred to as Non-Patent Document 10), and the like may be applied. 
     The clustering unit  112  performs clustering as described above, and supplies the sparse information (the model coefficients, the clustering result, or the like) to the interpolation processing unit  113 . 
       FIG.  3    is a diagram illustrating an example of a result obtained by visualizing part of model coefficients. For example, a sparse model coefficient  141  illustrated in A of  FIG.  3    is supplied to the interpolation processing unit  113  from the clustering unit  112 . A model coefficient  142  in B of  FIG.  3    is a model coefficient obtained by enlarging part of the model coefficient  141  in A of  FIG.  3   . A gray point group indicated in the model coefficient  142  indicates model coefficients of pixels at respective positions. Thus, the model coefficient  141  includes the sparse information (model coefficients of part of pixels). 
     C of  FIG.  3    is a diagram schematically illustrating a structure of this sparse model coefficient  141 . In C of  FIG.  3   , squares indicated by gray indicate pixels in which model coefficients exist. As illustrated in this example, the model coefficient  141  includes a model coefficient  144  for one pixel provided per area  143  of a predetermined size. When, for example, the area  143  is 4×4 pixels, a data amount of the model coefficient  141  is 1/16 of that in the dense case (model coefficients of all pixels). 
     The interpolation processing unit  113  performs image filtering on this sparse model coefficient  141  by using an image signal as a guide.  FIG.  4    is a diagram illustrating an example of part of an image used as this guide. For example, the interpolation processing unit  113  performs image filtering on the sparse model coefficient  141  by using an image  151  (A of  FIG.  4   ) included in the captured image  20  as a guide. An image  152  illustrated in B of  FIG.  4    is an image obtained by enlarging part of the image  151 . 
       FIG.  5    is a diagram illustrating an example of a result obtained by visualizing part of model coefficients obtained by this image filtering. By, for example, image filtering of the interpolation processing unit  113 , a model coefficient  161  illustrated in A of  FIG.  5    is obtained. A model coefficient  162  illustrated in B of  FIG.  5    is a model coefficient obtained by enlarging part of the model coefficient  161 . As is clear from comparison with the model coefficient  142  (B of  FIG.  3   ), the model coefficient  162  (i.e., the model coefficient  161 ) is in a dense state. 
     C of  FIG.  5    is a diagram schematically illustrating a structure of this model coefficient  161 . In C of  FIG.  5   , squares indicated by gray indicate pixels in which model coefficients exist. That is, in a case of this example, the model coefficient  161  includes model coefficients of all pixels. When, for example, an area  163  is 4×4 pixels, there are model coefficients  164  for 16 pixels in each area  163 . Therefore, a data amount of the model coefficient  161  (A of  FIG.  5   ) is 16 times as large as a data amount of the model coefficients  141  (A of  FIG.  3   ). 
     A clustering result  171  illustrated in A of  FIG.  6    illustrates an example of a clustering result derived by using this dense model coefficient  161 . A clustering result  172  illustrated in B of  FIG.  6    is a clustering result obtained by enlarging part of the clustering result  171 . In this way, by performing the image filtering, a dense clustering result is obtained from sparse model coefficients. 
     In, for example, a case of the structural examples in C of  FIG.  3    and C of  FIG.  5   , although a processing time of clustering for obtaining the sparse model coefficient  141  is different depending on a method used for clustering, even in, for example, a case of a simple k-means method, an order O of a calculation amount is O=(Nk) when the number of items of data is N and the number of times of iterations is a constant k, and the processing time is approximately 1/16 of the clustering processing time for obtaining the dense model coefficient  161 . When a processing time of the image filtering is taken into consideration, the entire processing time is approximately ⅓ to ¼ of a processing time in a case where the dense model coefficient  161  is obtained by clustering. That is, by applying sparse clustering and image filtering as described above, the image processing apparatus  100  can obtain a dense clustering result at a higher speed. That is, it is possible to suppress an increase in a processing time. 
     &lt;Flow of Clustering Processing&gt; 
     An example of a flow of the clustering processing executed by such an image processing apparatus  1000  will be described with reference to a flowchart of  FIG.  7   . When the clustering processing is started, the sampling pixel selection unit  111  obtains the captured image  20  in step S 101 . 
     In step S 102 , the sampling pixel selection unit  111  selects and determines sparse sampling pixels from the captured image obtained in step S 101 . 
     In step S 103 , the clustering unit  112  clusters the sparse sampling pixels determined in step S 102 . 
     In step S 104 , the interpolation processing unit  113  obtains the captured image  20 , performs image filtering on sparse information (model coefficients of learning and a clustering result) obtained by the processing in step S 103  by using this captured image  20  as a guide, interpolates this sparse information, and derives a dense clustering result. 
     In step S 105 , the interpolation processing unit  113  outputs the dense clustering result obtained by the processing in step S 104  as the clustering result  30 . When the processing in step S 105  ends, the clustering processing ends. 
     By performing each processing as described above, the image processing apparatus  100  can suppress an increase in a processing time of the image clustering. 
     &lt;Use of Field Information&gt; 
     For example, there is a method of, when analyzing vegetation (classification of vegetation, soil and the like) targeting at a field, clustering a stitching image obtained by stitching a plurality of captured images obtained by imaging this field from the sky. In such a case, it is unnecessary to cluster an area other than this field in the area included in the stitching image. However, in general, it is difficult to perform control to perform imaging focusing on a range of the field and not image an outside of the field, and the stitching image obtained by stitching the captured images includes areas outside the field, too. Hence, when the entire stitching image is clustered as a target, an area outside the field is also clustered, and therefore unnecessary processing is likely to increase a processing time unnecessarily. 
     Then, only pixels in the field are selected as sampling pixels (that is, pixels in an area outside the field are not selected as the sampling pixels). Field information (field boundary information) is information regarding a field, and is, for example, information that indicates a range of the field that is a target area on which image clustering is performed. Therefore, an area of the field included in the captured image is specified by using such field information, and the sampling pixels are selected only in this specified field. By so doing, it is possible to suppress an increase in unnecessary clustering, and suppress an increase in an unnecessary processing time. 
     &lt;Image Processing Apparatus&gt; 
       FIG.  8    is a block diagram illustrating a main configuration example of the image processing apparatus  100  in this case. The captured image  20  is a stitching image obtained by stitching a plurality of captured images obtained by imaging a clustering processing target field from the sky. As illustrated in  FIG.  8   , in this case, the image processing apparatus  100  includes a field area storage unit  201  in addition to the components illustrated in  FIG.  1   . 
     The field area storage unit  201  includes a storage medium, and stores information indicating an area (field area) of the processing target field in (the storage area of) this storage medium. This information indicating the field area may be any information. This information may be, for example, information that indicates a field area by using coordinate information (also referred to as GPS coordinate information) based on a global positioning system (GPS) or the like, information indicating which pixel of the captured image  20  is in the field area, or information other than these pieces of information. 
     The field area storage unit  201  supplies to the sampling pixel selection unit  111  information that is stored in (the storage area of) the storage medium of the field area storage unit  201  and indicates the field area as field information in response to, for example, a request of the sampling pixel selection unit  111 . 
     The sampling pixel selection unit  111  obtains this field information, and specifies the field area included in the captured image  20  on the basis of this field information. In a case of, for example, the field information indicating the field area by using the GPS coordinate information, the sampling pixel selection unit  111  compares and checks this field information and the GPS coordinate information indicating an imaging range of this captured image  20  included in the metadata of the captured image or the like, and thereby specifies pixels corresponding to the inside of the field area of the captured image  20 . 
     For example, a field area  211  that is part of the captured image as illustrated in A of  FIG.  9    is a processing target. The field area storage unit  201  stores information that indicates this field area  211 , and supplies this field information to the sampling pixel selection unit  111 . As illustrated in B of  FIG.  9   , the sampling pixel selection unit  111  selects sampling pixels in this field area  211  on the basis of this field information, and omits selection of the sampling pixels in an area other than the field area  211 . 
     In this case, too, a method of selecting the sampling pixels is similar to that in the case in  FIG.  1   . That is, the sampling pixel selection unit  111  selects sparse sampling pixels in the field area  211  indicated by the field information, and supplies the sparse sampling pixels to the clustering unit  112 . 
     By doing so, the sampling pixels that are processing targets of the clustering unit  112  include only the pixels in the field area. That is, the clustering unit  112  and the interpolation processing unit  113  can exclude pixels outside the field area from the processing targets. Consequently, the image processing apparatus  100  can suppress an increase in unnecessary clustering, and suppress an increase in an unnecessary processing time. 
     &lt;Flow of Clustering Processing&gt; 
     An example of a flow of the clustering processing in this case will be described with reference to a flowchart of  FIG.  10   . When the clustering processing is started, the sampling pixel selection unit  111  obtains the captured image  20  in step S 121 . Furthermore, the sampling pixel selection unit  111  obtains field information from the field area storage unit  201 . 
     In step S 122 , the sampling pixel selection unit  111  selects and determines sparse sampling pixels from the field area included in the captured image obtained in step S 121  on the basis of this field information. 
     Each processing in step S 123  to step S 125  is executed similar to each processing in step S 103  to step S 105  ( FIG.  7   ). When the processing in step S 125  ends, the clustering processing ends. 
     By performing each processing as described above, the image processing apparatus  100  can suppress an increase in a processing time of the image clustering. 
     &lt;Use of Stitching Information&gt; 
     In a case where, for example, a plurality of captured images obtained by imaging part of a field is stitched to generate a stitching image including the entire field as described above, the areas of each captured image generally include portions that are superimposed on each other. In other words, in general, it is difficult to control imaging such that the areas of each captured image are not superimposed on each other. 
     If sampling pixels are independently selected in each captured image, pixels in an area where a plurality of captured images is superimposed are likely to be selected as sampling pixels for each of a plurality of captured images. That is, pixels at the same position in a plurality of captured images are likely to be selected as sampling pixels. If there is a plurality of sampling pixels at the same position in a plurality of captured images in this way, clustering is performed a plurality of times for one position. Therefore, such redundant processing is likely to increase a processing time unnecessarily. 
     The stitching image is generated by selecting one of captured images of an area where such a plurality of captured images is superimposed, and connecting each captured image in a state where a plurality of captured images is not superimposed. That is, in each captured image, a stitched area is set such that each captured image is not superimposed on other captured images, and a stitched area of each captured image is stitched to each other to generate a stitching image. 
     Furthermore, in a case where a captured image includes an outside of the area that is a clustering target (e.g., an outside of the field area), pixels in such an area are likely to be selected as sampling pixels. In such a case, pixels in an area that does not need to be clustered are likely to be clustered, and unnecessary processing is likely to increase a processing time unnecessarily. 
     The above-described stitched areas can be set so as not to include such unnecessary areas. Therefore, by stitching the stitched area of each captured image, it is possible to generate a stitching image that does not include an area that is not a clustering processing target. 
     Hence, only pixels in such stitched areas are selected as sampling pixels. That is, in the area where a plurality of captured images is superimposed, sampling pixels are selected only in one of the captured images. Furthermore, sampling pixels are selected so as not to include pixels in areas that are not clustering targets. 
     The stitching information is information that includes information indicating such a stitched area of each captured image. That is, the stitching information includes information regarding an area in which captured images overlap, and that is a clustering processing target. Hence, the stitched area is specified by using such stitching information, and the sampling pixels are selected only in this specified stitched area. By so doing, it is possible to suppress an increase in redundant clustering and unnecessary clustering, and suppress an increase in an unnecessary processing time. 
     &lt;Image Processing Apparatus&gt; 
       FIG.  11    is a block diagram illustrating a main configuration example of the image processing apparatus  100  in this case. The captured image  20  is a stitching image obtained by stitching a plurality of captured images obtained by imaging a clustering processing target field from the sky. As illustrated in  FIG.  11   , in this case, the image processing apparatus  100  includes a stitching information storage unit  231  in addition to the components illustrated in  FIG.  1   . 
     The stitching information storage unit  231  includes a storage medium, and stores stitching information that includes information indicating a stitched area of each captured image in (a storage area of) this storage medium. This information indicating the stitched area may be any information. This information may be, for example, information that indicates the stitched area by using GPS coordinate information, or may be information that indicates the stitched area by using coordinate information in the captured image. 
     The stitching information storage unit  231  supplies the stitching information stored in (the storage area of) the storage medium of the stitching information storage unit  231  to the sampling pixel selection unit  111  in response to, for example, a request of the sampling pixel selection unit  111 . 
     The sampling pixel selection unit  111  obtains this stitching information, and specifies the stitched area of each captured image on the basis of this stitching information. In a case where, for example, sampling pixels are selected from a captured image  241  used to generate a stitching image  240  as illustrated in A of  FIG.  12   , the sampling pixel selection unit  111  specifies a stitched area such as a hatched portion illustrated in B of  FIG.  12    on the basis of the stitching information (by taking into account an overlap of a captured image  242  and a captured image  243  in surroundings), and selects sampling pixels in this stitched area. 
     In a case of the example of B of  FIG.  12   , the area where the captured image  241  and the captured image  242  are superimposed on each other is the stitched area of the captured image  242 , and therefore the sampling pixels are selected during processing of the captured image  242 . Similarly, the area where the captured image  241  and the captured image  243  are superimposed on each other is the stitched area of the captured image  243 , and therefore the sampling pixels are selected during processing of the captured image  243 . 
     Furthermore, in a case where, for example, sampling pixels are selected from a captured image  244  used to generate the stitching image  240  as illustrated in A of  FIG.  12   , the sampling pixel selection unit  111  specifies a stitched area such as a hatched portion illustrated in C of  FIG.  12    on the basis of the stitching information (by taking a clustering target area into account), and selects sampling pixels in this stitched area. 
     In a case of the example in C of  FIG.  12   , an area of the captured image  244  outside the stitching image  240  is an extra-stitched area. That is, an area of the captured image  244  inside the stitching image  240  is a stitched area. 
     In this case, too, a method of selecting the sampling pixels is similar to that in the case in  FIG.  1   . That is, the sampling pixel selection unit  111  selects sparse sampling pixels in the stitched area indicated by the stitching information, and supplies the sparse sampling pixels to the clustering unit  112 . 
     By so doing, the image processing apparatus  100  can prevent clustering from being performed for one position a plurality of times, and prevent clustering of unnecessary areas. That is, the image processing apparatus  100  can suppress an increase in redundant clustering and unnecessary clustering, and suppress an increase in an unnecessary processing time. 
     &lt;Flow of Clustering Processing&gt; 
     An example of a flow of the clustering processing in this case will be described with reference to a flowchart of  FIG.  13   . When the clustering processing is started, the sampling pixel selection unit  111  obtains the captured image  20  in step S 141 . Furthermore, the sampling pixel selection unit  111  obtains the stitching information from the stitching information storage unit  231 . 
     In step S 142 , the sampling pixel selection unit  111  selects and determines sparse sampling pixels from the stitched area of the captured image obtained in step S 141  on the basis of this stitching information. 
     Each processing in step S 143  to step S 145  is executed similar to each processing in step S 103  to step S 105  ( FIG.  7   ). When the processing in step S 145  ends, the clustering processing ends. 
     By performing each processing as described above, the image processing apparatus  100  can suppress an increase in a processing time of the image clustering. 
     &lt;Use of Flat Area Information&gt; 
     In general, a corner or edge portion of a captured image is a portion at which pixels having different classes from each other contact each other, and it is difficult to determine from which adjacent pixel to propagate a color. That is, clustering accuracy is higher in a flat area than in a corner or an edge. 
     Hence, sampling pixels are selected in the flat area, so that the pixels in the flat area can be clustered. That is, the flat area of the captured image is specified by using flat area information that is the information regarding the flat area, and the sampling pixels are selected in this flat area. By so doing, it is possible to obtain a more accurate clustering result. 
     &lt;Image Processing Apparatus&gt; 
       FIG.  14    is a block diagram illustrating a main configuration example of the image processing apparatus  100  in this case. As illustrated in  FIG.  14   , in this case, the image processing apparatus  100  includes a flat area detection unit  261  in addition to the components illustrated in  FIG.  1   . 
     The flat area detection unit  261  performs processing related to detection of a flat area. For example, the flat area detection unit  261  obtains the captured image  20 . 
     This captured image  20  may be the same as a captured image (i.e., a captured image to be clustered) supplied to the sampling pixel selection unit  111 , or a captured image (i.e., a captured image used as the guide) supplied to the interpolation processing unit  113 , or may be a captured image whose time and range are substantially the same time and substantially the same range as those of the captured image to be clustered and the captured image used as the guide, and that is different from this captured image to be clustered and the captured image used as the guide. For example, the captured image  20  may be another captured image obtained by another imaging at substantially the same time and at substantially the same angle of view as imaging for obtaining the captured image to be clustered and the captured image used as the guide. For example, the captured image  20  of the wavelength range of visible light (RGB) may be supplied to the sampling pixel selection unit  111  and the interpolation processing unit  113 , and the captured image  20  obtained by imaging the wavelength range of invisible light such as near-infrared ray may be supplied to the flat area detection unit  261 . 
     Furthermore, the flat area detection unit  261  detects a flat area of this captured image. Furthermore, the flat area detection unit  261  supplies flat area information that is information indicating the detected flat area to the sampling pixel selection unit  111 . 
     The sampling pixel selection unit  111  obtains this flat area information, and selects sampling pixels in the flat area included in captured image  20  on the basis of this flat area information. In this case, too, a method of selecting the sampling pixels is similar to that in the case in  FIG.  1   . That is, the sampling pixel selection unit  111  selects sparse sampling pixels in the flat area, and supplies the sparse sampling pixels to the clustering unit  112 . 
     By so doing, the image processing apparatus  100  can obtain a more accurate clustering result. 
     &lt;Flow of Clustering Processing&gt; 
     An example of a flow of the clustering processing in this case will be described with reference to a flowchart of  FIG.  15   . When the clustering processing is started, the sampling pixel selection unit  111  obtains the captured image  20  in step S 161 . 
     In step S 162 , the flat area detection unit  261  obtains the captured image  20 , and detects a flat area of this captured image  20 . 
     In step S 163 , the sampling pixel selection unit  111  selects and determines the sparse sampling pixels from the flat area detected in step S 162  in the captured image obtained in step S 161 . 
     Each processing in step S 164  to step S 166  is executed similar to each processing in step  3103  to step S 105  ( FIG.  7   ). When the processing in step  3166  ends, the clustering processing ends. 
     By executing each processing as described above, the image processing apparatus  100  can obtain a more accurate clustering result. 
     &lt;Use of a Plurality of Pieces of Information&gt; 
     Although it has been described above that the image processing apparatus  100  selects sampling pixels by using one of the assist information, the stitching information, and the flat area information, the image processing apparatus  100  is not limited to this, and, for example, may select sampling pixels by using at least two or more pieces of the field information, the stitching information, and the flat area information. By so doing, it is possible to obtain an effect in a case of where each information is used. Naturally, the image processing apparatus  100  may select the sampling pixels by using one or more of these pieces of information and, in addition, information other than the above-described information. 
     2. Second Embodiment 
     &lt;Wide Area Clustering and Sparse Local Clustering&gt; 
     According to image clustering, by using, for example, information obtained by wide area clustering that is clustering of sparse pixels in a wide area (also referred to as a global area), local clustering that is clustering of pixels in a local area (also referred to as a local area) may be performed. 
     For example, as illustrated on a right side in  FIG.  16   , a stitching image  270  (a captured image of the entire field) obtained by stitching (stitched areas of) a plurality of captured images  271  obtained by imaging the field is clustered to analyze vegetation of this field. 
     According to such clustering, the entire field (the entire stitching image  270 ) is a wide area, and wide area clustering is performed as prior learning on this wide area (i.e., the entire stitching image  270 ). For example, sparse wide area sampling pixels  272  (white circles in  FIG.  16   ) are selected from the entire stitching image  270  (the entire wide area) as wide area sampling pixels that are wide area clustering target sampling pixels. Then, the wide area sampling pixels  272  are clustered (i.e., wide area clustering). 
     Next, each captured image  271  (frame image) is set as a local area, and local clustering is performed as additional learning on each captured image  271  by using information (e.g., a model of learning, a clustering result, or the like) obtained by the wide area clustering. In a case where, for example, a captured image  271 A is a processing target, local sampling pixels are selected as local sampling pixels that are local clustering target sampling pixels from this captured image  271 A. Furthermore, the local sampling pixels are clustered (i.e., local clustering). 
     Note that local sampling pixels may also be selected from captured images in surroundings of the processing target captured image  271 A (e.g., the one previously processed captured image  271 B before the captured image  271 A, one subsequently captured image  271 C after the captured image  271 A, and the like). Furthermore, this additional learning may be performed by using information obtained by additional learning of one previous captured image (i.e., information obtained by local clustering of the captured image  271 B (e.g., a model of learning, a clustering result, or the like.)) (that is, sequential learning may be performed). 
     By using the information obtained by the wide area clustering in this way, it is possible to use a model estimated once, so that it is possible to obtain a model that is stable (or that is influenced little by fluctuation of an initial value) at a high speed during local clustering. Furthermore, it is possible to obtain clustering results at a high speed by targeting at sparse sampling pixels during wide area clustering, too. Consequently, it is possible to suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     The present technology described in the first embodiment is applied to such a clustering method. For example, according to the above-described local clustering, sparse local sampling pixels are clustered, sparse information (e.g., a model of learning, a clustering result, or the like.) obtained by this clustering is interpolated by image filtering that uses an image signal as a guide, and thereby a dense clustering result is derived. By so doing, it is possible to suppress for local clustering an increase in a processing time as described in the first embodiment. 
     &lt;Image Processing Apparatus&gt; 
       FIG.  17    is a block diagram illustrating a main configuration example of an image processing apparatus in this case. 
     An image processing apparatus  300  illustrated in  FIG.  17    is an apparatus that performs image clustering similar to an image processing apparatus  100 . That is, the image processing apparatus  300  receives a captured image  20  as input, performs the image clustering on this captured image  20 , and outputs a clustering result  30  of this image clustering. 
     Similar to the case of the first embodiment, the captured image  20  may be, for example, a stitching image obtained by stitching a plurality of captured images (P 1  to Pn). Furthermore, the captured image  20  may be a moving image including a plurality of frame images. Furthermore, the captured image  20  may be a file (captured image group) obtained by integrating a plurality of captured images into one, or may be one captured image. Naturally, the captured image  20  may be an image other than a captured image (e.g., a CG image or the like). Furthermore, this captured image  20  may be an image of a wavelength range of visible light (RGB), or may be an image obtained by imaging a wavelength range of invisible light such as near-infrared light. Furthermore, the captured image  20  may be both of these images. 
     In the following description, it is assumed that the captured image  20  corresponds to the stitching image  270  that is obtained by stitching the captured images  271  obtained by imaging part of the field as in the example of  FIG.  16   , and that corresponds to the entire field. Furthermore, the following description will describe that a wide area (global area) is this entire stitching image  270 , and a local area (local area) is each captured image  271  (captured images corresponding to one frame). 
     Note that  FIG.  17    illustrates main elements such as processing units and data flows, and the elements illustrated in  FIG.  17    are not necessarily all. That is, in this image processing apparatus  300 , there may be a processing unit that is not illustrated as a block in  FIG.  17   , or there may be processing or a data flow that is not illustrated as an arrow or the like in  FIG.  17   . 
     As illustrated in  FIG.  17   , the image processing apparatus  300  includes a prior learning unit  311 , an additional learning unit  312 , and a coefficient storage unit  313 . 
     The prior learning unit  311  performs image clustering (wide area clustering) on a wide area (e.g., the entire captured image  20 ) as prior learning. In this case, the prior learning unit  311  performs wide area clustering on sparse pixels. The prior learning unit  311  includes a sampling pixel selection unit  321  and a clustering unit  322 . 
     The sampling pixel selection unit  321  performs processing related to selection of wide area sampling pixels that are wide area clustering target pixels. For example, the sampling pixel selection unit  321  obtains the captured image  20 . Furthermore, the sampling pixel selection unit  321  selects the wide area sampling pixels from this captured image  20  such that the wide area sampling pixels are in a sparse state. 
     The sampling pixel selection unit  321  supplies the selected sparse wide area sampling pixels to the clustering unit  322 . 
     The clustering unit  322  performs processing related to wide area clustering. For example, the clustering unit  322  obtains the sparse wide area sampling pixels supplied from the sampling pixel selection unit  321 . The clustering unit  322  performs wide area clustering (prior learning) on these obtained sparse wide area sampling pixels as processing targets. This wide area clustering method is arbitrary. For example, a Gaussian Mixture Model (GMM), a k-means method, or the like may be applied to the prior learning. 
     The clustering unit  322  supplies information obtained by this prior learning (wide area clustering) such as model coefficients of the prior learning, a wide area clustering result, or the like to the coefficient storage unit  313 . 
     Furthermore, as additional learning performed by using information obtained by prior learning as an initial value, the additional learning unit  312  performs image clustering (local clustering) on a local area (e.g., each stitched captured image) by using information obtained by wide area clustering as an initial value. Similar to the image processing apparatus  100 , the additional learning unit  312  clusters sparse sampling pixels, performs image filtering on sparse information obtained by this clustering by using the captured image  20  as a guide, and thereby derives a dense clustering result. 
     Similar to the image processing apparatus  100  ( FIG.  1   ), the additional learning unit  312  includes a sampling pixel selection unit  111 , a clustering unit  112 , and an interpolation processing unit  113 . 
     Similar to the case in  FIG.  1   , the sampling pixel selection unit  111  performs processing related to selection of sparse sampling pixels. For example, the sampling pixel selection unit  111  obtains the captured image  20 . In this case, the entire stitching image may be supplied to the sampling pixel selection unit  111 , or each captured image (frame image) that makes up the stitching image may be supplied one by one to the sampling pixel selection unit  111 . 
     The sampling pixel selection unit  111  selects sparse sampling pixels (local sampling pixels) from each captured image (local area). In this case, the sampling pixel selection unit  111  may select captured images (local areas) in surroundings of a processing target captured image such as one previous processing target captured image (local area) and one subsequent processing target captured image (local area) as local sampling pixel selection targets. That is, the sampling pixel selection unit  111  may select sparse local sampling pixels from the processing target local area or the local areas in the surroundings of the processing target local area. 
     The sampling pixel selection unit  111  supplies the selected local sampling pixels to the clustering unit  112 . 
     Similar to the case in  FIG.  1   , the clustering unit  112  performs local clustering on these sparse local sampling pixels, and supplies the obtained sparse information (e.g., model coefficients of additional learning, a wide area clustering result, or the like) to the interpolation processing unit  113 . In this regard, the clustering unit  112  in this case obtains information obtained by prior learning (wide area clustering) stored in the coefficient storage unit  313  such as model coefficients of prior learning, a wide area clustering result, or the like, sets information (the model coefficients of the prior learning, the wide area clustering result, or the like) obtained by this prior learning as an initial value, and performs local clustering. 
     That is, the clustering unit  112  obtains the sparse local sampling pixels supplied from the sampling pixel selection unit  111 . Furthermore, the clustering unit  112  supplies sparse information (e.g., model coefficients of the prior learning, a wide area clustering result, or the like) stored in the coefficient storage unit  313 , and obtained by the prior learning (wide area clustering). The clustering unit  112  sets these obtained sparse sampling as processing targets, sets information (the model coefficients of the prior learning, the wide area clustering result, or the like) obtained by this prior learning as an initial value, and performs local clustering as additional learning. The clustering unit  112  supplies sparse information (e.g., the model coefficients of the additional learning, the local clustering result, or the like) obtained by this additional learning (local clustering) to the interpolation processing unit  113 . 
     Note that the clustering unit  112  may further perform local clustering (current local clustering) on a current processing target local area by using information, too, obtained by performing local clustering (previous local clustering) on one previous processing target local area. That is, the clustering unit  112  may perform sequential learning by using a previous learning model, a learning result, or the like as the additional learning. 
     In that case, the clustering unit  112  causes the coefficient storage unit  313  to hold information (e.g., the model coefficients of the sequential learning, the local clustering result, or the like) obtained by the sequential learning. That is, the clustering unit  112  obtains, from the coefficient storage unit  313  the information obtained by the prior learning and, in addition, information obtained by previous sequential learning, too, and performs local clustering (sequential learning). Furthermore, the clustering unit  112  supplies the information (e.g., the model coefficients of the sequential learning, the local clustering result, or the like) obtained by this sequential learning to the interpolation processing unit  113 , and supplies and stores the information to and in the coefficient storage unit  313 . The information stored in this coefficient storage unit  313  is used for next sequential learning (local clustering for a next processing target local area). 
     According to such sequential learning, it is possible to derive in the local area at a high speed a clustering result that reflects a wide area clustering result and a clustering result of an adjacent local area. Consequently, it is possible to suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     In other words, in a case where the above-described sequential learning is not performed as the additional learning, it is possible to omit supply (i.e., an arrow  341  in  FIG.  17   ) of information (the model coefficients of the additional learning, the local clustering result, or the like) obtained by the additional learning to the coefficient storage unit  313 . 
     Similar to the case in  FIG.  1   , the interpolation processing unit  113  performs processing related to interpolation of the sparse information. For example, the interpolation processing unit  113  obtains the sparse information (the model coefficients of the additional learning, the clustering result, or the like) supplied from the clustering unit  112 . Furthermore, the interpolation processing unit  113  performs image filtering (interpolation processing) on this sparse information by using an image signal as a guide, and derives a dense clustering result as a local clustering result. The interpolation processing unit  113  outputs the clustering result  30  (dense clustering result) obtained by this interpolation processing as an image processing result of the image processing apparatus  100  to an outside of the image processing apparatus  100 . 
     The coefficient storage unit  313  obtains the information (the model coefficients of the prior learning and the wide area clustering result) supplied from (the clustering unit  322  of) the prior learning unit  311  and obtained by the prior learning, and stores the information in (the storage area of) the storage medium of the coefficient storage unit  313 . Furthermore, in a case where the additional learning unit  312  performs sequential learning, the coefficient storage unit  313  obtains the information (the model coefficients of the sequential learning and the wide area clustering result) supplied from (the clustering unit  112  of) this additional learning unit  312  and obtained by the sequential learning, and stores the information in (the storage area of) the storage medium of the coefficient storage unit  313 . Furthermore, the coefficient storage unit  313  supplies the information obtained by the prior learning and the information obtained by the sequential learning and stored in (the storage area of) the storage medium of the coefficient storage unit  313  to the clustering unit  112  on the basis of, for example, a request of the clustering unit  112 . 
     The image processing apparatus  300  employs such a configuration, and can use a model estimated once by using information obtained by wide area clustering, so that it is possible to obtain a model that is stable (or that is influenced little by fluctuation of an initial value) at a high speed during local clustering. Furthermore, the image processing apparatus  100  can perform wide area clustering on sparse sampling pixels as targets, and obtain a clustering result at a high speed. Furthermore, the image processing apparatus  100  performs local sampling pixels on the sparse local sampling pixels, performs image filtering that uses an image as a guide on sparse information obtained by this local sampling, and thereby derives a dense clustering result at a high speed. Consequently, the image processing apparatus  300  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     &lt;Flow of Clustering Processing&gt; 
     An example of a flow of the clustering processing in this case will be described with reference to a flowchart of  FIG.  18   . When the clustering processing is started, in step S 201 , the sampling pixel selection unit  321  of the prior learning unit  311  sets an image of a global area (wide area) as a global image, and obtains the captured image  20  (e.g., the stitching image  270 ) of a stitching image. 
     In step S 202 , the sampling pixel selection unit  321  selects and determines sparse wide area sampling pixels from the global image obtained in step S 201 . 
     In step S 203 , the clustering unit  322  performs wide area clustering as prior learning on the sparse wide area sampling pixels determined in step S 202 . 
     In step S 204 , the coefficient storage unit  313  stores the information (e.g., the model coefficients of the prior learning or the wide area clustering result) obtained by the prior learning performed in step S 203 . 
     In step S 205 , the sampling pixel selection unit  111  of the additional learning unit  312  obtains a processing target local image from a plurality of local images (images of local areas (local areas)) included in the global image obtained in step S 201 . Furthermore, the sampling pixel selection unit  111  selects and determines sparse local sampling pixels from this processing target local image. 
     In step S 206 , the clustering unit  112  performs local clustering as additional learning on the sparse local sampling pixels determined in step S 205 . In this case, the clustering unit  112  performs sequential learning by using the information stored in the coefficient storage unit  313  and obtained by the prior learning, and the information obtained by previous additional learning (sequential learning). 
     In step S 207 , the coefficient storage unit  313  stores the information (e.g., the model coefficients of the prior learning or the local clustering result) obtained by the additional learning (sequential learning) performed in step S 206 . 
     In step S 208 , the interpolation processing unit  113  obtains the captured image  20 , performs image filtering on sparse information (the model coefficients of the additional learning and the clustering result) obtained by the processing in step S 206  by using this captured image  20  as a guide, interpolates this sparse information, and derives a dense clustering result. 
     In step S 209 , the additional learning unit  312  determines whether or not the additional learning has been performed on all local images. In a case where it is determined that there is an unprocessed local image, the processing returns to step S 205  to execute subsequent processing with respect to a next local image as a processing target. That is, each processing in step S 205  to step S 209  is executed for each local image. In a case where it is determined in step S 209  that all the local images have been processed, the processing proceeds to step S 210 . 
     In step S 210 , the interpolation processing unit  113  outputs the clustering result  30  optimized as described above. When the processing in step S 210  ends, the clustering processing ends. 
     By executing each processing as described above, the image processing apparatus  300  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     In other words, note that, in a case where sequential learning is not performed as additional learning, it is possible to omit the processing in step S 207 . Furthermore, in step S 206 , the clustering unit  112  performs the additional learning by using the information stored in the coefficient storage unit  313  and obtained by the prior learning. 
     &lt;Reference of Wide Area Sampling Pixels&gt; 
     Note that local sampling pixels may be selected by taking a selection result of wide area sampling pixels into account. For example, the local sampling pixels may be selected from pixels other than the wide area sampling pixels. That is, the wide area sampling pixels may be excluded from local sampling pixel candidates. 
     Furthermore, in a case where the additional learning unit  312  (clustering unit  112 ) performs sequential learning as the additional learning for performing current local clustering by using information obtained by previous local clustering, the sampling pixel selection unit  111  may further select current local sampling pixels by taking a selection result of previous local sampling pixels into account. For example, the current local sampling pixels may be selected from pixels other than the previous local sampling pixels. That is, the previous local sampling pixels may be excluded from the current local sampling pixel candidates. 
     As described above, by excluding the wide area sampling pixels and performing local clustering during additional learning, it is possible to suppress an increase in redundancy of clustering, and further suppress a decrease in robustness of image clustering. Furthermore, by excluding previous local sampling pixels and performing current local clustering during sequential learning, it is possible to suppress an increase in redundancy of clustering, and further suppress a decrease in robustness of image clustering. Consequently, it is possible to suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     &lt;Image Processing Apparatus&gt; 
       FIG.  19    is a block diagram illustrating a main configuration example of the image processing apparatus  300  in this case. As illustrated in  FIG.  19   , the image processing apparatus  300  in this case includes a sampling pixel storage unit  351  in addition to the components in the example in  FIG.  17   . 
     In this case, the sampling pixel selection unit  321  of the prior learning unit  311  supplies the selected wide area sampling pixels to the clustering unit  322 , and supplies the selected wide area sampling pixels to the sampling pixel storage unit  351 , too. 
     The sampling pixel storage unit  351  includes a storage medium, and performs processing related to storage of sampling pixels. For example, the sampling pixel storage unit  351  obtains the wide area sampling pixels supplied from (the sampling pixel selection unit  321  of) the prior learning unit  311 , and stores the wide area sampling pixels in (the storage area of) the storage medium of the sampling pixel storage unit  351 . 
     Furthermore, the sampling pixel storage unit  351  supplies the wide area sampling pixels stored in (the storage area of) the storage medium of the sampling pixel storage unit  351  to the sampling pixel selection unit  111  on the basis of, for example, a request of the sampling pixel selection unit  111 . 
     In this case, the sampling pixel selection unit  111  obtains the wide area sampling pixels stored in the sampling pixel storage unit  351 . The sampling pixel selection unit  111  selects sparse local sampling pixels from pixels other than these wide area sampling pixels in a processing target local area (frame image), and supplies the sparse local sampling pixels to the clustering unit  112 . By so doing, the clustering unit  112  can suppress an increase in redundancy of clustering, and further suppress a decrease in robustness of image clustering. 
     Note that, in a case where the additional learning unit  312  performs sequential learning, the sampling pixel selection unit  111  of the additional learning unit  312  supplies the selected local sampling pixels to the clustering unit  112 , and supplies the selected local sampling pixels to the sampling pixel storage unit  351 , too. 
     In this case, the sampling pixel storage unit  351  obtains the local sampling pixels supplied from (the sampling pixel selection unit  111  of) this additional learning unit  312 , and stores the information in (the storage area of) the storage medium of the sampling pixel storage unit  351 . Furthermore, the sampling pixel storage unit  351  supplies the wide area sampling pixels and the previous local sampling pixels stored in (the storage area of) the storage medium of the sampling pixel storage unit  351  to the sampling pixel selection unit  111  on the basis of, for example, a request of the sampling pixel selection unit  111 . 
     Then, the sampling pixel selection unit  111  obtains these wide area sampling pixels and previous local sampling pixels from the sampling pixel storage unit  351 . The sampling pixel selection unit  111  selects sparse local sampling pixels from pixels other than these wide area sampling pixels and previous local sampling pixels in a processing target local area (frame image), and supplies the sparse local sampling pixels to the clustering unit  112 . By so doing, the clustering unit  112  can suppress an increase in redundancy of clustering, and further suppress a decrease in robustness of image clustering. 
     In other words, in a case where the above-described sequential learning is not performed as the additional learning, it is possible to omit supply (i.e., an arrow  361  in  FIG.  19   ) of the local sampling pixels to the sampling pixel storage unit  351 . 
     &lt;Flow of Clustering Processing&gt; 
     An example of a flow of the clustering processing executed by the image processing apparatus  300  in this case will be described with reference to a flowchart of  FIG.  20   . When the clustering processing is started, each processing in step S 251  and step S 252  is executed similar to each processing in step S 201  and step S 202  ( FIG.  18   ). 
     In step S 253 , the sampling pixel storage unit  351  stores the sparse wide area sampling pixels determined in step S 252 . 
     When the processing in step S 253  ends, each processing in step S 254  and step S 255  is executed similar to each processing in step S 203  and step S 204  ( FIG.  18   ). 
     In step S 256 , the sampling pixel selection unit  111  of the additional learning unit  312  obtains a processing target local image from a local image group included in the global image obtained in step S 251 . Furthermore, the sampling pixel selection unit  111  selects sparse local sampling pixels from pixels other than the wide area sampling pixels and the previous local sampling pixels in this processing target local image. 
     In step S 257 , the sampling pixel storage unit  351  stores the sparse local sampling pixels (current local sampling pixels) determined in step S 256 . 
     When step S 257  ends, each processing in step S 258  to step S 260  is executed similar to each processing in step S 206  to step S 208  ( FIG.  18   ). 
     In step S 261 , the additional learning unit  312  determines whether or not the additional learning has been performed on all local images. In a case where it is determined that there is an unprocessed local image, the processing returns to step S 256  to execute subsequent processing with respect to a next local image as a processing target. That is, each processing in step S 256  to step S 261  is executed for each local image. In a case where it is determined in step S 261  that all the local images have been processed, the processing proceeds to step S 262 . 
     In step S 262 , the interpolation processing unit  113  outputs the clustering result  30  optimized as described above. When the processing in step S 262  ends, the clustering processing ends. 
     By executing each processing as described above, the image processing apparatus  300  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     In other words, note that, in a case where sequential learning is not performed as additional learning, it is possible to omit the processing in step S 255  and step S 259 . Furthermore, in step S 256 , the sampling pixel selection unit  111  selects sampling pixels by using the wide area sampling pixels stored in the sampling pixel storage unit  351 . Furthermore, in step S 258 , the clustering unit  112  performs the additional learning by using the information stored in the coefficient storage unit  313  and obtained by the prior learning. 
     &lt;Other Components&gt; 
     Note that the prior learning unit  311  may be a component of another apparatus in the image processing apparatus  300  in  FIG.  17   . That is, the image processing apparatus  300  may include the additional learning unit  312  and the coefficient storage unit  313 . In this case, the coefficient storage unit  313  obtains and stores sparse information (model coefficients of prior learning, a clustering result, or the like) obtained by the prior learning unit  311  of) the another apparatus. Furthermore, the additional learning unit  312  performs local clustering on sparse local sampling pixels by using the sparse information stored in the coefficient storage unit  313  and obtained by (the prior learning unit  311  of) the another apparatus. 
     Furthermore, the prior learning unit  311  and the coefficient storage unit  313  may be components of another apparatus in the image processing apparatus  300  in  FIG.  17   . That is, the image processing apparatus  300  may include the additional learning unit  312 . In this case, the additional learning unit  312  performs local clustering on sparse local sampling pixels by using the sparse information stored in (the coefficient storage unit  313 ) of the another apparatus and obtained by (the prior learning unit  311  of) the another apparatus. 
     In both of the cases, similar to the case in  FIG.  17   , the image processing apparatus  300  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     Furthermore, the prior learning unit  311  may be a component of another apparatus in the image processing apparatus  300  in  FIG.  19   . That is, the image processing apparatus  300  may include the additional learning unit  312 , the coefficient storage unit  313 , and the sampling pixel storage unit  351 . In this case, the coefficient storage unit  313  obtains and stores sparse information (model coefficients of prior learning, a clustering result, or the like) obtained by the prior learning unit  311  of) the another apparatus. Furthermore, the sampling pixel storage unit  351  obtains and stores sparse wide area sampling pixels selected by (the prior learning unit  311  of) the another apparatus. Furthermore, the additional learning unit  312  selects sparse local sampling pixels on the basis of the sparse wide area sampling pixels stored in the sampling pixel storage unit  351  and selected by (the prior learning unit  311  of) the another apparatus, and performs local clustering on these selected sparse local sampling pixels by using the sparse information stored in the coefficient storage unit  313  and obtained by (the prior learning unit  311  of) the another apparatus. 
     Furthermore, the prior learning unit  311  and the coefficient storage unit  313  may be components of another apparatus in the image processing apparatus  300  in  FIG.  19   . That is, the image processing apparatus  300  may include the additional learning unit  312  and the sampling pixel storage unit  351 . In this case, the sampling pixel storage unit  351  obtains and stores wide area sampling pixels selected by (the prior learning unit  311  of) the another apparatus. Furthermore, the additional learning unit  312  selects sparse local sampling pixels on the basis of the sparse wide area sampling pixels stored in the sampling pixel storage unit  351  and selected by (the prior learning unit  311  of) the another apparatus, and performs local clustering on these selected sparse local sampling pixels by using the sparse information stored in (the coefficient storage unit  313  of) of the another apparatus and obtained by (the prior learning unit  311  of) the another apparatus. 
     Furthermore, the prior learning unit  311  and the sampling pixel storage unit  351  may be components of another apparatus in the image processing apparatus  300  in  FIG.  19   . That is, the image processing apparatus  300  may include the additional learning unit  312  and the coefficient storage unit  313 . In this case, the coefficient storage unit  313  obtains and stores information (model coefficients of prior learning, a clustering result, or the like) obtained by (the prior learning unit  311  of) the another apparatus. Furthermore, the additional learning unit  312  selects sparse local sampling pixels on the basis of the sparse wide area sampling pixels stored in (the sampling pixel storage unit  351 ) of the another apparatus and selected by (the prior learning unit  311  of) the another apparatus, and performs local clustering on these selected sparse local sampling pixels by using the sparse information stored in the coefficient storage unit  313  and obtained by (the prior learning unit  311  of) the another apparatus. 
     Furthermore, the prior learning unit  311 , the coefficient storage unit  313 , and the sampling pixel storage unit  351  may be components of another apparatus in the image processing apparatus  300  in  FIG.  19   . That is, the image processing apparatus  300  may include the additional learning unit  312 . In this case, the additional learning unit  312  selects sparse local sampling pixels on the basis of the sparse wide area sampling pixels stored in (the sampling pixel storage unit  351  of) the another apparatus and selected by (the prior learning unit  311  of) the another apparatus, and performs local clustering on these selected sparse local sampling pixels by using the sparse information stored in (the coefficient storage unit  313  of) of the another apparatus and obtained by (the prior learning unit  311  of) the another apparatus. 
     In both of the cases, similar to the case in  FIG.  19   , the image processing apparatus  300  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     Naturally, in each of these cases, the additional learning unit  312  can perform the above-described sequential learning as the additional learning similar to the cases in  FIGS.  17  and  19   . 
     Furthermore, the image processing apparatus  300  may select the local sampling pixels by using at least one or more of field information, stitching information, and flat area information described in the first embodiment. By doing so, it is possible to obtain an effect in a case where each information is used for the additional learning. Naturally, the image processing apparatus  300  may select the sampling pixels by using one or more of these pieces of information and, in addition, information other than the above-described information. 
     Note that, although the present embodiment has described the case where the captured image  20  is a stitching image, the present embodiment is not limited to this, and the captured image  20  may be a moving image including a plurality of frame images, may be a file (captured image group) obtained by integrating a plurality of captured images into one, or may be one captured image. Naturally, the captured image  20  may be an image other than a captured image (e.g., a CG image or the like). Furthermore, this captured image  20  may be an image of a wavelength range of visible light (RGB), or may be an image obtained by imaging a wavelength range of invisible light such as near-infrared light. Furthermore, the captured image  20  may be both of these images. 
     Furthermore, a wide area (global area) may not be the entire captured image  20 , or a local area (local area) may not be captured images corresponding to one frame. The local area may be an area of the wide area that is narrower than the wide area. As long as this applies, each of the wide area and the local area may be any area in the captured image  20 . 
     3. Third Embodiment 
     &lt;Wide Area Clustering and Dense Local Clustering&gt; 
     As described above in the second embodiment, according to image clustering, local clustering may be performed by using, for example, sparse information obtained by wide area clustering of sparse wide area sampling pixels. Then, this local clustering may be performed on local sampling pixels in a dense state. That is, instead of performing local clustering on sparse local sampling pixels, performing image filtering that uses an image signal as a guide on the obtained sparse information, and thereby deriving a dense clustering result as in the second embodiment, local clustering may be performed on local sampling pixels in a dense state. 
     In this case, too, similar to the case of the second embodiment, a model estimated once by wide area clustering can be used, so that it is possible to obtain a model that is stable (or that is influenced little by fluctuation of an initial value) at a high speed during local clustering. Furthermore, it is possible to obtain clustering results at a high speed by targeting at sparse sampling pixels during wide area clustering, too. Consequently, it is possible to suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     &lt;Image Processing Apparatus&gt; 
       FIG.  21    is a block diagram illustrating a main configuration example of an image processing apparatus in this case. An image processing apparatus  400  illustrated in  FIG.  21    is an apparatus that performs image clustering similar to an image processing apparatus  300 . That is, the image processing apparatus  400  receives a captured image  20  as input, performs the image clustering on this captured image  20 , and outputs a clustering result  30  of this image clustering. 
     Similar to the case of the second embodiment, the captured image  20  may be, for example, a stitching image obtained by stitching a plurality of captured images (P 1  to Pn). Furthermore, the captured image  20  may be a moving image including a plurality of frame images. Furthermore, the captured image  20  may be a file (captured image group) obtained by integrating a plurality of captured images into one, or may be one captured image. Naturally, the captured image  20  may be an image other than a captured image (e.g., a CG image or the like). Furthermore, this captured image  20  may be an image of a wavelength range of visible light (RGB), or may be an image obtained by imaging a wavelength range of invisible light such as near-infrared light. Furthermore, the captured image  20  may be both of these images. 
     In the following description, it is assumed that the captured image  20  corresponds to a stitching image  270  that is obtained by stitching captured images  271  obtained by imaging part of a field as in the example of  FIG.  16   , and that corresponds to the entire field. Furthermore, the following description will describe that a wide area (global area) is this entire stitching image  270 , and a local area (local area) is each captured image  271  (captured images corresponding to one frame). 
     Note that  FIG.  21    illustrates main elements such as processing units and data flows, and the elements illustrated in  FIG.  21    are not necessarily all. That is, in this image processing apparatus  400 , there may be a processing unit that is not illustrated as a block in  FIG.  21   , or there may be processing or a data flow that is not illustrated as an arrow or the like in  FIG.  21   . 
     As illustrated in  FIG.  21   , the image processing apparatus  400  includes a prior learning unit  311 , an additional learning unit  312 , and a coefficient storage unit  313  similar to the image processing apparatus  300  ( FIG.  17   ). 
     Similar to the case of the image processing apparatus  300  ( FIG.  17   ), the prior learning unit  311  includes a sampling pixel selection unit  321  and a clustering unit  322 , and performs wide area clustering on sparse wide area sampling pixels as prior learning, and supplies information obtained by this prior learning to the coefficient storage unit  313 . The information obtained by this prior learning is information that is obtained by wide area clustering, and corresponds to each sampling pixel (i.e., a sparse state). For example, the information may be model coefficients of prior learning, may be a clustering result, or may be both of the model coefficients of learning and the clustering result. 
     The coefficient storage unit  313  employs a configuration similar to that of the image processing apparatus  300  ( FIG.  17   ), and stores sparse information (e.g., the model coefficients of prior learning, a wide area clustering result, or the like) supplied from the prior learning unit  311 . Furthermore, the coefficient storage unit  313  supplies the stored sparse information to (a clustering unit  412  of) the additional learning unit  312  in response to, for example, a request of (the clustering unit  412  of) the additional learning unit  312 . 
     Similar to the case of the image processing apparatus  300  ( FIG.  17   ), the additional learning unit  312  performs additional learning by using as an initial value the sparse information (e.g., the model coefficients of prior learning, the wide area clustering result, or the like) obtained by the prior learning. In this regard, the additional learning unit  312  in this case performs local clustering as additional learning on dense local sampling pixels, and derives a dense clustering result. 
     This local clustering method is arbitrary. For example, a Structure-constrained Gaussian Mixture Model (SC-GMM) may be applied to this additional learning. According to the SC-GAMM, optimization that takes image structure information into account for clustering in a color space is derived. For example, a structure of a texture or an edge is used to obtain an adjacent relationship between pixels, and classification is performed on the basis of this adjacent relationship. By so doing, it is possible to perform more accurate clustering. 
     As illustrated in  FIG.  21   , the additional learning unit  312  in this case includes a sampling pixel selection unit  411 , the clustering unit  412 , and an optimization unit  413 . 
     The sampling pixel selection unit  411  performs processing related to selection of local sampling pixels. For example, the sampling pixel selection unit  411  obtains the captured image  20 . In this case, an entire stitching image may be supplied to the sampling pixel selection unit  411 , or each captured image (frame image) that makes up a stitching image may be supplied one by one to the sampling pixel selection unit  411 . 
     Furthermore, the sampling pixel selection unit  411  selects part or all of pixels of each captured image (local area) as local sampling pixels. In this case, the sampling pixel selection unit  411  selects the local sampling pixels such that the local sampling pixels are in a dense state. Note that the sampling pixel selection unit  411  selects captured images (local areas) in surroundings of a processing target captured image such as one previous processing target captured image (local area) and one subsequent processing target captured image (local area) as local sampling pixel selection targets. That is, the sampling pixel selection unit  411  may select dense local sampling pixels from a processing target local area or local areas in the surroundings of the processing target local area. 
     The sampling pixel selection unit  411  supplies the selected dense local sampling pixels to the clustering unit  412 . 
     The clustering unit  412  performs processing related to local clustering. For example, the clustering unit  412  obtains dense local sampling pixels supplied from the sampling pixel selection unit  411 . Furthermore, the clustering unit  412  supplies sparse information (e.g., model coefficients of the prior learning, a wide area clustering result, or the like) stored in the coefficient storage unit  313 , and obtained by prior learning (wide area clustering). 
     The clustering unit  412  sets the sparse information obtained by this prior learning as an initial value, and performs local clustering that is dense local sampling. The clustering unit  412  supplies information obtained by this additional learning (local clustering of the dense local sampling pixels) to the optimization unit  413 . The information obtained by this additional learning is information that is obtained by local clustering, and corresponds to each sampling pixel (that is, that is in a dense state). For example, the information may be model coefficients of the additional learning, may be a clustering result, or may be both of the model coefficients of learning and the clustering result. 
     Note that the clustering unit  412  may further perform local clustering (current local clustering) on a current processing target local area by using information, too, obtained by performing local clustering (previous local clustering) on one previous processing target local area. That is, the clustering unit  412  may perform sequential learning that uses a previous learning model, a clustering result, or the like as the additional learning. 
     In that case, the clustering unit  412  causes the coefficient storage unit  313  to hold dense information (e.g., model coefficients of the sequential learning, a local clustering result, or the like) obtained by the sequential learning. Furthermore, the clustering unit  412  obtains, from the coefficient storage unit  313 , the sparse information obtained by the prior learning and, in addition, dense information obtained by previous sequential learning, too, and performs local clustering (sequential learning). Furthermore, the clustering unit  412  supplies the information (e.g., the model coefficients of the sequential learning, the local clustering result, or the like) obtained by this sequential learning to the optimization unit  413 , and supplies and stores the information to and in the coefficient storage unit  313 . The information stored in this coefficient storage unit  313  is used for next sequential learning (local clustering for a next processing target local area). 
     According to such sequential learning, it is possible to derive in the local area at a high speed a clustering result that reflects a wide area clustering result and a clustering result of an adjacent local area. Consequently, it is possible to suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     In other words, in a case where the above-described sequential learning is not performed as the additional learning, it is possible to omit supply (i.e., an arrow  421  in  FIG.  21   ) of information (the model coefficients of the additional learning, the local clustering result, or the like) obtained by the additional learning to the coefficient storage unit  313 . 
     The optimization unit  413  performs processing related to optimization of a clustering result. For example, the optimization unit  413  obtains the information (e.g., the model coefficients of the additional learning, the local clustering result, or the like) supplied from the clustering unit  412  and obtained by the additional learning. Furthermore, the optimization unit  413  obtains the captured image  20 . 
     This captured image  20  may be the same as the captured image  20  (i.e., the captured image to be clustered) supplied to the sampling pixel selection unit  321  and the sampling pixel selection unit  411 , or may be a captured image whose time and range are substantially the same time and substantially the same range as those of the captured image to be clustered, and that is different from this captured image to be clustered. For example, the captured image  20  may be another captured image obtained by another imaging at substantially the same time and at substantially the same angle of view as imaging for obtaining the captured image to be clustered. For example, the captured image  20  of the wavelength range of visible light (RGB) may be supplied to the sampling pixel selection unit  321  and the sampling pixel selection unit  411 , and the captured image  20  obtained by imaging the wavelength range of invisible light such as near-infrared ray may be supplied to the optimization unit  413 . 
     The optimization unit  413  optimizes the dense information obtained by the additional learning by using this captured image  20 , and derives an optimized dense clustering result. For example, the optimization unit  413  obtains an adjacent relationship between pixels by taking image structure information (a structure of a texture or an edge) of this captured image  20  into account, and optimizes model coefficients and a clustering result on the basis of this adjacent relationship. 
     The optimization unit  413  outputs the clustering result  30  (i.e., the clustering result on which the optimization processing has been performed) obtained by this processing as an image processing result of the image processing apparatus  400  to an outside of the image processing apparatus  400 . 
     The image processing apparatus  400  employs such a configuration, so that it is possible to perform local clustering by using a model estimated once by wide area clustering. Consequently, the image processing apparatus  400  can obtain a model that is stable (or that is influenced little by fluctuation of an initial value) at a high speed during local clustering. Furthermore, the image processing apparatus  400  employs such a configuration, so that it is possible to obtain a clustering result at a high speed by targeting at sparse sampling pixels during wide area clustering, too. Consequently, the image processing apparatus  400  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     A clustering result  431  illustrated in A of  FIG.  22    illustrates an example of a clustering result derived by the image processing apparatus  400 . Furthermore, a clustering result  432  illustrated in B of  FIG.  22    illustrates an example of a clustering result derived by the image processing apparatus  300 . That is, each image processing apparatus can obtain a substantially similar clustering result. That is, similar to the case of the image processing apparatus  300 , the image processing apparatus  400  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     &lt;Flow of Clustering Processing&gt; 
     An example of a flow of the clustering processing executed by this image processing apparatus  400  will be described with reference to a flowchart of  FIG.  23   . When the clustering processing is started, each processing in step S 301  to step S 304  is executed similar to each processing in step S 201  to step S 204  ( FIG.  18   ). 
     In step S 305 , the sampling pixel selection unit  411  of the additional learning unit  312  obtains a processing target local image from a local image group included in the global image obtained in step S 301 . Furthermore, the sampling pixel selection unit  411  selects and determines dense local sampling pixels from this processing target local image. 
     In step S 306 , the clustering unit  412  performs local clustering as additional learning on the dense local sampling pixels determined in step S 305 . In this case, the clustering unit  412  performs sequential learning by using the information stored in the coefficient storage unit  313  and obtained by the prior learning, and the information obtained by previous additional learning (sequential learning). 
     In step S 307 , the coefficient storage unit  313  stores the information (e.g., the model coefficients of the prior learning or the local clustering result) obtained by the additional learning (sequential learning) performed in step S 306 . 
     In step S 308 , the optimization unit  413  stores the information (e.g., the model coefficients and the local clustering result of the additional learning) obtained by the additional learning (sequential learning) performed in step S 306 , and derives an optimized clustering result. 
     In step S 309 , the additional learning unit  312  determines whether or not the additional learning has been performed on all local images. In a case where it is determined that there is an unprocessed local image, the processing returns to step S 305  to execute subsequent processing with respect to a next local image as a processing target. That is, each processing in step S 305  to step S 309  is executed for each local image. In a case where it is determined in step S 309  that all the local images have been processed, the processing proceeds to step S 310 . 
     In step S 310 , the optimization unit  413  outputs the clustering result  30  optimized as described above. When the processing in step S 310  ends, the clustering processing ends. 
     By executing each processing as described above, the image processing apparatus  400  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     In other words, note that, in a case where sequential learning is not performed as additional learning, it is possible to omit the processing in step S 307 . Furthermore, in step S 306 , the clustering unit  412  performs the additional learning by using the information stored in the coefficient storage unit  313  and obtained by the prior learning. 
     &lt;Reference of Wide Area Sampling Pixels&gt; 
     Note that, similar to the case of the image processing apparatus  300  described in the second embodiment, local sampling pixels may be selected by taking a selection result of wide area sampling pixels into account. For example, the local sampling pixels may be selected from pixels other than the wide area sampling pixels. That is, the wide area sampling pixels may be excluded from local sampling pixel candidates. 
     Furthermore, in a case where the additional learning unit  312  (clustering unit  412 ) performs sequential learning as the additional learning for performing current local clustering by using information obtained by previous local clustering, the sampling pixel selection unit  411  may further select current local sampling pixels by taking a selection result of previous local sampling pixels into account. For example, the current local sampling pixels may be selected from pixels other than the previous local sampling pixels. That is, the previous local sampling pixels may be excluded from the current local sampling pixel candidates. 
     As described above, by excluding the wide area sampling pixels and performing local clustering during additional learning, it is possible to suppress an increase in redundancy of clustering, and further suppress a decrease in robustness of image clustering. Furthermore, by excluding previous local sampling pixels and performing current local clustering during sequential learning, it is possible to suppress an increase in redundancy of clustering, and further suppress a decrease in robustness of image clustering. Consequently, it is possible to suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     &lt;Image Processing Apparatus&gt; 
       FIG.  24    is a block diagram illustrating a main configuration example of the image processing apparatus  400  in this case. As illustrated in  FIG.  24   , similar to the case of the image processing apparatus  300  in  FIG.  19   , the image processing apparatus  400  in this case includes a sampling pixel storage unit  351  in addition to the components in the example in  FIG.  21   . 
     In this case, the sampling pixel selection unit  321  of the prior learning unit  311  supplies the selected wide area sampling pixels to the clustering unit  322 , and supplies the selected wide area sampling pixels to the sampling pixel storage unit  351 , too. 
     Similar to the case in  FIG.  19   , the sampling pixel storage unit  351  includes a storage medium, and performs processing related to storage of sampling pixels. For example, the sampling pixel storage unit  351  obtains the wide area sampling pixels supplied from (the sampling pixel selection unit  321  of) the prior learning unit  311 , and stores the wide area sampling pixels in (the storage area of) the storage medium of the sampling pixel storage unit  351 . 
     Furthermore, the sampling pixel storage unit  351  supplies the wide area sampling pixels stored in (the storage area of) the storage medium of the sampling pixel storage unit  351  to the sampling pixel selection unit  411  on the basis of, for example, a request of the sampling pixel selection unit  411 . 
     In this case, the sampling pixel selection unit  411  obtains the wide area sampling pixels stored in the sampling pixel storage unit  351 . The sampling pixel selection unit  411  selects dense local sampling pixels from pixels other than these wide area sampling pixels in a processing target local area (frame image), and supplies the dense local sampling pixels to the clustering unit  412 . By so doing, the clustering unit  412  can suppress an increase in redundancy of clustering, and further suppress a decrease in robustness of image clustering. 
     Note that, in a case where the additional learning unit  312  performs sequential learning, the sampling pixel selection unit  411  of the additional learning unit  312  supplies the selected local sampling pixels to the clustering unit  412 , and supplies the selected local sampling pixels to the sampling pixel storage unit  351 , too. 
     In this case, the sampling pixel storage unit  351  obtains the local sampling pixels supplied from (the sampling pixel selection unit  411  of) this additional learning unit  312 , and stores the information in (the storage area of) the storage medium of the sampling pixel storage unit  351 . Furthermore, the sampling pixel storage unit  351  supplies the wide area sampling pixels and the previous local sampling pixels stored in (the storage area of) the storage medium of the sampling pixel storage unit  351  to the sampling pixel selection unit  411  on the basis of, for example, a request of the sampling pixel selection unit  411 . 
     Then, the sampling pixel selection unit  411  obtains these wide area sampling pixels and previous local sampling pixels from the sampling pixel storage unit  351 . The sampling pixel selection unit  411  selects dense local sampling pixels from pixels other than these wide area sampling pixels and previous local sampling pixels in a processing target local area (frame image), and supplies the dense local sampling pixels to the clustering unit  412 . By so doing, the clustering unit  412  can suppress an increase in redundancy of clustering, and further suppress a decrease in robustness of image clustering. 
     In other words, in a case where the above-described sequential learning is not performed as the additional learning, it is possible to omit supply (i.e., an arrow  441  in  FIG.  24   ) of the local sampling pixels to the sampling pixel storage unit  351 . 
     &lt;Flow of Clustering Processing&gt; 
     An example of a flow of the clustering processing executed by the image processing apparatus  400  in this case will be described with reference to a flowchart of  FIG.  25   . When the clustering processing is started, each processing in step S 351  and step S 352  is executed similar to each processing in step S 301  and step S 302  ( FIG.  23   ). 
     In step S 353 , the sampling pixel storage unit  351  stores the sparse wide area sampling pixels determined in step S 352 . 
     When the processing in step S 353  ends, each processing in step S 354  and step S 355  is executed similar to each processing in step S 303  and step S 304  ( FIG.  23   ). 
     In step S 356 , the sampling pixel selection unit  411  of the additional learning unit  312  obtains a processing target local image from a local image group included in the global image obtained in step S 351 . Furthermore, the sampling pixel selection unit  411  selects dense local sampling pixels from pixels other than the wide area sampling pixels and the previous local sampling pixels in this processing target local image. 
     In step S 357 , the sampling pixel storage unit  351  stores the dense local sampling pixels (current local sampling pixels) determined in step S 356 . 
     When step S 357  ends, each processing in step S 358  to step S 360  is executed similar to each processing in step S 306  to step S 308  ( FIG.  23   ). 
     In step S 361 , the additional learning unit  312  determines whether or not the additional learning has been performed on all local images. In a case where it is determined that there is an unprocessed local image, the processing returns to step S 356  to execute subsequent processing with respect to a next local image as a processing target. That is, each processing in step S 356  to step S 361  is executed for each local image. In a case where it is determined in step S 361  that all the local images have been processed, the processing proceeds to step S 362 . 
     In step S 362 , the optimization unit  413  outputs the clustering result  30  optimized as described above. When the processing in step S 362  ends, the clustering processing ends. 
     By executing each processing as described above, the image processing apparatus  400  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     In other words, note that, in a case where sequential learning is not performed as additional learning, it is possible to omit the processing in step S 355  and step S 359 . Furthermore, in step S 356 , the sampling pixel selection unit  411  selects sampling pixels by using the wide area sampling pixels stored in the sampling pixel storage unit  351 . Furthermore, in step S 358 , the clustering unit  412  performs the additional learning by using the information stored in the coefficient storage unit  313  and obtained by the prior learning. 
     &lt;Other Components&gt; 
     Note that the prior learning unit  311  may be a component of another apparatus in the image processing apparatus  400  in  FIG.  21   . That is, the image processing apparatus  400  may include the additional learning unit  312  and the coefficient storage unit  313 . In this case, the coefficient storage unit  313  obtains and stores sparse information (model coefficients of prior learning, a clustering result, or the like) obtained by the prior learning unit  311  of) the another apparatus. Furthermore, the additional learning unit  312  performs local clustering on dense local sampling pixels by using the sparse information stored in the coefficient storage unit  313  and obtained by (the prior learning unit  311  of) the another apparatus. 
     Furthermore, the prior learning unit  311  and the coefficient storage unit  313  may be components of another apparatus in the image processing apparatus  400  in  FIG.  21   . That is, the image processing apparatus  400  may include the additional learning unit  312 . In this case, the additional learning unit  312  performs local clustering on dense local sampling pixels by using the sparse information stored in (the coefficient storage unit  313 ) of the another apparatus and obtained by (the prior learning unit  311  of) the another apparatus. 
     In both of the cases, similar to the case in  FIG.  21   , the image processing apparatus  400  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     Furthermore, the prior learning unit  311  may be a component of another apparatus in the image processing apparatus  400  in  FIG.  24   . That is, the image processing apparatus  400  may include the additional learning unit  312 , the coefficient storage unit  313 , and the sampling pixel storage unit  351 . In this case, the coefficient storage unit  313  obtains and stores sparse information (model coefficients of prior learning, a clustering result, or the like) obtained by the prior learning unit  311  of) the another apparatus. Furthermore, the sampling pixel storage unit  351  obtains and stores sparse wide area sampling pixels selected by (the prior learning unit  311  of) the another apparatus. Furthermore, the additional learning unit  312  selects dense local sampling pixels on the basis of the sparse wide area sampling pixels stored in the sampling pixel storage unit  351  and selected by (the prior learning unit  311  of) the another apparatus, and performs local clustering on these selected dense local sampling pixels by using the sparse information stored in the coefficient storage unit  313  and obtained by (the prior learning unit  311  of) the another apparatus. 
     Furthermore, the prior learning unit  311  and the coefficient storage unit  313  may be components of another apparatus in the image processing apparatus  400  in  FIG.  24   . That is, the image processing apparatus  400  may include the additional learning unit  312  and the sampling pixel storage unit  351 . In this case, the sampling pixel storage unit  351  obtains and stores wide area sampling pixels selected by (the prior learning unit  311  of) the another apparatus. Furthermore, the additional learning unit  312  selects dense local sampling pixels on the basis of the sparse wide area sampling pixels stored in the sampling pixel storage unit  351  and selected by (the prior learning unit  311  of) the another apparatus, and performs local clustering on these selected dense local sampling pixels by using the sparse information stored in (the coefficient storage unit  313  of) of the another apparatus and obtained by (the prior learning unit  311  of) the another apparatus. 
     Furthermore, the prior learning unit  311  and the sampling pixel storage unit  351  may be components of another apparatus in the image processing apparatus  400  in  FIG.  24   . That is, the image processing apparatus  400  may include the additional learning unit  312  and the coefficient storage unit  313 . In this case, the coefficient storage unit  313  obtains and stores information (model coefficients of prior learning, a clustering result, or the like) obtained by (the prior learning unit  311  of) the another apparatus. Furthermore, the additional learning unit  312  selects dense local sampling pixels on the basis of the sparse wide area sampling pixels stored in (the sampling pixel storage unit  351  of) the another apparatus and selected by (the prior learning unit  311  of) the another apparatus, and performs local clustering on these selected dense local sampling pixels by using the sparse information stored in the coefficient storage unit  313  and obtained by (the prior learning unit  311  of) the another apparatus. 
     Furthermore, the prior learning unit  311 , the coefficient storage unit  313 , and the sampling pixel storage unit  351  may be components of another apparatus in the image processing apparatus  400  in  FIG.  24   . That is, the image processing apparatus  400  may include the additional learning unit  312 . In this case, the additional learning unit  312  selects dense local sampling pixels on the basis of the sparse wide area sampling pixels stored in (the sampling pixel storage unit  351  of) the another apparatus and selected by (the prior learning unit  311  of) the another apparatus, and performs local clustering on these selected dense local sampling pixels by using the sparse information stored in (the coefficient storage unit  313  of) of the another apparatus and obtained by (the prior learning unit  311  of) the another apparatus. 
     In both of the cases, similar to the case in FIG.  24 , the image processing apparatus  400  can suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     Naturally, in each of these cases, the additional learning unit  312  can perform the above-described sequential learning as the additional learning similar to the cases in  FIGS.  21  and  24   . 
     Furthermore, the image processing apparatus  400  may select the local sampling pixels by using at least one or more of field information, stitching information, and flat area information described in the first embodiment. By doing so, it is possible to obtain an effect in a case where each information is used for the additional learning. Naturally, the image processing apparatus  400  may select the sampling pixels by using one or more of these pieces of information and, in addition, information other than the above-described information. 
     Note that, although the present embodiment has described the case where the captured image  20  is a stitching image, the present embodiment is not limited to this, and the captured image  20  may be a moving image including a plurality of frame images, may be a file (captured image group) obtained by integrating a plurality of captured images into one, or may be one captured image. Naturally, the captured image  20  may be an image other than a captured image (e.g., a CG image or the like). Furthermore, this captured image  20  may be an image of a wavelength range of visible light (RGB), or may be an image obtained by imaging a wavelength range of invisible light such as near-infrared light. Furthermore, the captured image  20  may be both of these images. 
     Furthermore, a wide area (global area) may not be the entire captured image  20 , or a local area (local area) may not be captured images corresponding to one frame. The local area may be an area of the wide area that is narrower than the wide area. As long as this applies, each of the wide area and the local area may be any area in the captured image  20 . 
     4. Fourth Embodiment 
     &lt;Application to Vegetation Area Analysis&gt; 
     An image processing apparatus (an image processing apparatus  100 , an image processing apparatus  300 , or an image processing apparatus  400 ) described above in the first embodiment to the third embodiment can be used to, for example, analyze a vegetation area. 
     &lt;Image Processing Apparatus&gt; 
     An image processing apparatus  500  illustrated in  FIG.  26    is a diagram illustrating an example of an embodiment of an image processing apparatus to which the present technology is applied. This image processing apparatus  500  is an apparatus that analyzes a vegetation area, and, for example, receives as an input a captured image  20  obtained by imaging a field or the like, analyzes a vegetation area by using image clustering for this captured image  20 , and outputs vegetation area information  520  that is an analysis result of the analysis. 
     Similar to the case of each of the above-described embodiments, the captured image  20  may be, for example, a stitching image obtained by stitching a plurality of captured images (P 1  to Pn). Furthermore, the captured image  20  may be a moving image including a plurality of frame images. Furthermore, the captured image  20  may be a file (captured image group) obtained by integrating a plurality of captured images into one, or may be one captured image. Furthermore, this captured image  20  may be an image of a wavelength range of visible light (RGB), or may be an image obtained by imaging a wavelength range of invisible light such as near-infrared light. Furthermore, the captured image  20  may be both of these images. 
     Note that  FIG.  26    illustrates main elements such as processing units and data flows, and the elements illustrated in  FIG.  26    are not necessarily all. That is, in this image processing apparatus  500 , there may be a processing unit that is not illustrated as a block in  FIG.  26   , or there may be processing or a data flow that is not illustrated as an arrow or the like in  FIG.  26   . 
     As illustrated in  FIG.  26   , the image processing apparatus  500  includes a clustering unit  511  and a vegetation area determination unit  512 . The clustering unit  511  performs clustering on the captured image  20 , and derives a dense clustering result. The above-described image processing apparatus can be applied to this clustering unit  511 . That is, the clustering unit  511  employs a configuration similar to that of one of the above-described image processing apparatuses, and derives a clustering result from the captured image  20  by performing similar processing (clustering). The clustering unit  511  supplies this clustering result to the vegetation area determination unit  512 . 
     The vegetation area determination unit  512  performs processing related to determination of a vegetation area. For example, the vegetation area determination unit  512  obtains the clustering result supplied from the clustering unit  511 . Furthermore, the vegetation area determination unit  512  obtains the captured image  20 . The vegetation area determination unit  512  determines the vegetation area by using these pieces of information, and outputs the vegetation area information  520  that is an analysis result of the determination. By so doing, the image processing apparatus  500  can generate the analysis result of the vegetation area at a higher speed while suppressing a decrease in robustness. 
     &lt;Flow of Clustering Processing&gt; 
     An example of a flow of the clustering processing in this case will be described with reference to a flowchart of  FIG.  27   . When the clustering processing is started, the clustering unit  511  obtains the captured image  20  in step S 501 . 
     In step S 502 , the clustering unit  511  performs the clustering processing, and obtains a dense clustering result. The above-described clustering processing can be applied to this clustering process. That is, the clustering unit  511  derives the dense clustering result by performing the clustering processing according to a flow similar to each one of the above-described flowcharts. 
     In step S 503 , the vegetation area determination unit  512  determines a vegetation area on the basis of the clustering result obtained in step S 502 , and obtains the vegetation area information  520 . 
     In step S 504 , the vegetation area determination unit  512  outputs the vegetation area information  520  obtained by the processing in step S 503 . When the processing in step S 504  ends, the clustering processing ends. 
     By executing each processing as described above, the image processing apparatus  500  can obtain a more accurate clustering result. Consequently, the image processing apparatus  500  can generate the vegetation area information  520  at a higher speed while suppressing a decrease in robustness. 
     5. Fifth Embodiment 
     &lt;Application to Medical Device&gt; 
     The present technology described above in the first embodiment to the third embodiment is not limited to the above-described vegetation area analysis, and can be applied to an arbitrary technology in an arbitrary field. For example, the present technology can be used for a medical device. 
     For example, a computed tomography (CT) inspection apparatus irradiates a human body with X-rays while rotating, collects transmitted X-ray intensities by a detector, analyzes and calculates obtained data by a computer, and creates various images. As illustrated in, for example, A of  FIG.  28   , the CT inspection apparatus can obtain a tomographic image of an arbitrary position and direction such as an XY plane, a YZ plane, and an XZ plane by irradiating a patient  601  with X-rays. For example, a plurality of CT images  611  is obtained as a CT image  611 - 1  to a CT image  611 - 5  illustrated in B of  FIG.  28   . The present technology may be applied to clustering of a plurality of CT images  611  obtained by such CT inspection. 
     In this case, as in, for example, A of  FIG.  29   , one entire CT image  651  (CT Slice) may be set as a wide area (global area), for example, a predetermined partial area  652  of this CT image  651  such as a block may be set as a local area (local area), and this clustering may be performed by applying the above-described present technology. That is, in this case, both the wide area and the local area are two-dimensional planes, and each CT image is clustered one by one. In this case, it is possible to perform processing similar to the case of a captured image of a field described above. 
     In a case where, for example, the method described in the third embodiment is applied, wide area clustering (prior learning) is performed on sparse wide area sampling pixels selected from the entire CT image  651 , local clustering (additional learning) is performed on dense local sampling pixels in each block by using the obtained sparse information (model coefficients of the prior learning, a clustering result, or the like) as an initial value, and a dense clustering result is derived. 
     Furthermore, in a case where, for example, the method described in the second embodiment is applied, wide area clustering (prior learning) is performed on sparse wide area sampling pixels selected from the entire CT image  651 , local clustering (additional learning) is performed on sparse local sampling pixels in each block by using the obtained sparse information (model coefficients of the prior learning, a clustering result, or the like) as an initial value, the obtained sparse information (model coefficients of the additional learning, a clustering result, or the like) is interpolated by filtering that uses a two-dimensional image as a guide, and a dense clustering result is derived. 
     In this case, filtering performs two-dimensional processing of propagating colors of adjacent pixels on the two-dimensional plane (that is, on the same CT image). A processing target pixel x i  is derived from a peripheral pixel x j  on the same CT image by using, for example, following equation (1). Note that W i, j  is a weight coefficient, and is derived as in following equation (2). 
     
       
         
           
             
               
                 
                   minimize 
                   
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                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
       
         
           
             
               
                 
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                   2 
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     Furthermore, in a case where, for example, the method described in the first embodiment is applied, sparse sampling pixels selected from the entire CT image  651  are clustered, the obtained sparse information (model coefficients of learning, a clustering result, or the like) is interpolated by filtering that uses a two-dimensional image as a guide, and a dense clustering result is derived. 
     In this case, filtering performs two-dimensional processing of propagating colors of adjacent pixels on the two-dimensional plane (that is, on the same CT image). The processing target pixel x i  is derived from the surrounding pixel x j  on the same CT image by using, for example, above-described equation (1). Note that W i, j  is a weight coefficient, and is derived as in above-described equation (2). 
     Furthermore, as illustrated in, for example, B of  FIG.  29   , the CT image  651  (CT Slice) may be set as a local area (local area), a CT volume  653  (CT volume) that is a three-dimensional area including a plurality of CT images  651  may be set as a wide area (global area), and this clustering may be performed by applying the above-described present technology. That is, in this case, the wide area is set as a set of two-dimensional planes (three-dimensional area), a local area is the two-dimensional plane, and the CT volume is collectively clustered. 
     In a case where, for example, the method described in the third embodiment is applied, wide area clustering (prior learning) is performed on sparse wide area sampling pixels selected from the CT volume  653  (all CT images  651 ), local clustering (additional learning) is performed on dense local sampling pixels in each CT image  651  by using the obtained sparse information (model coefficients of the prior learning, a clustering result, or the like) as an initial value, and a dense clustering result is derived. 
     Furthermore, in a case where, for example, the method described in the second embodiment is applied, wide area clustering (prior learning) is performed on sparse wide area sampling pixels selected from the CT volume  653  (all CT images  651 ), local clustering (additional learning) is performed on sparse local sampling pixels in each CT image  651  by using the obtained sparse information (model coefficients of the prior learning, a clustering result, or the like) as an initial value, the obtained sparse information (model coefficients of the additional learning, a clustering result, or the like) is interpolated by filtering that uses a two-dimensional image as a guide, and a dense clustering result is derived. 
     In this case, filtering performs three-dimensional processing of propagating colors of adjacent pixels on a three-dimensional space. That is, in this case, it is possible to not only propagate colors of adjacent pixels on the same CT image, but also propagate colors of adjacent pixels on adjacent CT images. The processing target pixel x i  is derived from the surrounding pixel x j  on the same CT image or an adjacent CT image by using, for example, above-described equation (1). Note that the weighting coefficient W i, j  in this case is derived as in following expression (3). 
     
       
         
           
             
               
                 
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                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
       
         
           
             
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             indicates text missing or illegible when filed 
           
         
       
     
     Furthermore, in a case where, for example, the method described in the first embodiment is applied, sparse sampling pixels selected from the CT volume  653  (all CT images  651 ) are clustered, the obtained sparse information (model coefficients of learning, a clustering result, or the like) is interpolated by filtering that uses a two-dimensional image as a guide, and a dense clustering result is derived. 
     In this case, the filtering performs the above-described three-dimensional processing. The processing target pixel x i  is derived from the surrounding pixel x j  on the same CT image by using, for example, above-described equation (1). Note that W i, j  is a weight coefficient, and is derived as in above-described equation (3). 
     Furthermore, as illustrated in, for example, C of  FIG.  29   , the CT volume  653  is set as a wide area (global area), a voxel  654  (voxel) that is a three-dimensional area of a predetermined size obtained by dividing this CT volume  653  is set as a local area (local area), and this clustering may be performed by applying the above-described present technology. That is, in this case, both the wide area and the local area are three-dimensional areas, and the CT volume is collectively clustered. 
     In a case where, for example, the method described in the third embodiment is applied, wide area clustering (prior learning) is performed on sparse wide area sampling pixels selected from the CT volume  653  (all CT images  651 ), local clustering (additional learning) is performed on dense local sampling pixels in each voxel  654  by using the obtained sparse information (model coefficients of the prior learning, a clustering result, or the like) as an initial value, and a dense clustering result is derived. 
     Furthermore, in a case where, for example, the method described in the second embodiment is applied, wide area clustering (prior learning) is performed on sparse wide area sampling pixels selected from the CT volume  653  (all CT images  651 ), local clustering (additional learning) is performed on sparse local sampling pixels in each voxel  654  by using the obtained sparse information (model coefficients of the prior learning, a clustering result, or the like) as an initial value, the obtained sparse information (model coefficients of the additional learning, a clustering result, or the like) is interpolated by filtering that uses 3D data as a guide, and a dense clustering result is derived. 
     In this case, filtering performs three-dimensional processing of propagating colors of adjacent pixels on a three-dimensional space. That is, in this case, the color of the adjacent pixel in the three-dimensional space is propagated. The processing target pixel x i  is derived from the surrounding pixel x j  on the same CT image or an adjacent CT image by using, for example, above-described equation (1). Note that the weighting coefficient W i, j  in this case is derived as in above-described expression (3). 
     Furthermore, in a case where, for example, the method described in the first embodiment is applied, sparse sampling pixels selected from the CT volume  653  (all CT images  651 ) are clustered, the obtained sparse information (model coefficients of the additional learning, a clustering result, or the like) is interpolated by filtering that uses 3D data as a guide, and a dense clustering result is derived. 
     In this case, the filtering performs the above-described three-dimensional processing. The processing target pixel x i  is derived from the surrounding pixel x j  on the same CT image by using, for example, above-described equation (1). Note that W i, j  is a weight coefficient, and is derived as in above-described equation (3). 
     In a case of CT images that make up the CT volume, a correlation of an image structure between images is generally high, and therefore even filtering of three-dimensional processing can obtain a more accurate clustering result similar to the case of two-dimensional processing. Therefore, even in the case where the present technology is applied to the described above medical device, it is possible to suppress an increase in a processing time while suppressing a decrease in robustness of image clustering. 
     &lt;Image Processing Apparatus&gt; 
       FIG.  30    illustrates a main configuration example of the image processing apparatus in this case. An image processing apparatus  700  illustrated in  FIG.  30    is an apparatus that clusters CT images (CT volumes), receives a captured image  710  that is a CT image (CT volume) as an input, clusters this captured image  710 , and outputs the clustered CT image  720  as a clustering result of this clustering. 
     Note that  FIG.  30    illustrates main elements such as processing units and data flows, and the elements illustrated in  FIG.  30    are not necessarily all. That is, in this image processing apparatus  700 , there may be a processing unit that is not illustrated as a block in  FIG.  30   , or there may be processing or a data flow that is not illustrated as an arrow or the like in  FIG.  30   . 
     As illustrated in  FIG.  30   , the image processing apparatus  700  includes a clustering unit  711  and an analysis unit  712 . The clustering unit  711  clusters the captured image  710 , and derives a dense clustering result. The above-described image processing apparatus can be applied to this clustering unit  711 . That is, the clustering unit  711  employs a configuration similar to that of each one of the above-described image processing apparatuses, and derives a clustering result from the captured image  710  by performing similar processing (clustering). The clustering unit  711  supplies this clustering result to the analysis unit  712 . 
     The analysis unit  712  performs processing related to image analysis on the basis of the clustering result. For example, the analysis unit  712  obtains the clustering result supplied from the clustering unit  711 . Furthermore, the analysis unit  712  obtains the captured image  710 . The analysis unit  712  analyzes a structure of a human body that is a subject or the like in the captured image  710  on the basis of this clustering result, and images the structure. The analysis unit  712  outputs the generated CT image  720  as an analysis result. By so doing, the image processing apparatus  700  can generate the CT image  720  at a higher speed while suppressing a decrease robustness. 
     &lt;Flow of Clustering Processing&gt; 
     An example of a flow of the clustering processing in this case will be described with reference to a flowchart of  FIG.  31   . When the clustering processing is started, the clustering unit  711  obtains the captured image  710  in step S 701 . 
     In step S 702 , the clustering unit  711  performs the clustering processing, and obtains a dense clustering result. The above-described clustering processing can be applied to this clustering process. That is, the clustering unit  711  derives the dense clustering result by performing the clustering processing according to a flow similar to each one of the above-described flowcharts. 
     In step S 703 , the analysis unit  712  analyzes an image on the basis of the clustering result obtained in step S 702 . 
     In step S 704 , the analysis unit  712  outputs the CT image  720  as an analysis result obtained by the processing in step S 703 . When the processing in step S 704  ends, the clustering processing ends. 
     By executing each processing as described above, the image processing apparatus  700  can obtain a more accurate clustering result. Consequently, the image processing apparatus  700  can generate the CT image  720  at a higher speed while suppressing a decrease robustness. 
     6. Supplementary Note 
     &lt;Computer&gt; 
     The above-described series of processing can be executed by hardware or can be executed by software. In a case where the series of processing is executed by software, a program that configures this software is installed to a computer. Here, the computer includes, for example, a computer incorporated in dedicated hardware, and a general-purpose personal computer that can execute various functions by installing various programs. 
       FIG.  32    is a block diagram illustrating a configuration example of hardware of the computer that executes the above-described series of processing by the program. 
     In a computer  900  illustrated in  FIG.  32   , a central processing unit (CPU)  901 , a read only memory (ROM)  902 , and a random access memory (RAM)  903  are mutually connected via a bus  904 . 
     The bus  904  is further connected with an input/output interface  910 , too. The input/output interface  910  is connected with an input unit  911 , an output unit  912 , a storage unit  913 , a communication unit  914 , and a drive  915 . 
     The input unit  911  includes, for example, a keyboard, a mouse, a microphone, a touch panel, an input terminal, and the like. The output unit  912  includes, for example, a display, a speaker, an output terminal, and the like. The storage unit  913  includes, for example, a hard disk, a RAM disk, a nonvolatile memory, and the like. The communication unit  914  includes, for example, a network interface and the like. The drive  915  drives a removable medium  921  such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory. 
     In the computer configured as described above, for example, the CPU  901  loads a program stored in the storage unit  913  into the RAM  903  via the input/output interface  910  and the bus  904 , executes the program, and thereby performs the above-described series of processing. The RAM  903  also appropriately stores data and the like that are necessary for the CPU  901  to execute various types of processing. 
     For example, the program executed by the computer can be recorded in the removable medium  921  as a package medium or the like, and applied. In this case, the program can be installed in the storage unit  913  via the input/output interface  910  by attaching the removable medium  921  to the drive  915 . 
     Furthermore, this program can be also provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital satellite broadcasting. In this case, the program can be received by the communication unit  914  and installed in the storage unit  913 . 
     In addition, this program can be installed in the ROM  902  or the storage unit  913  in advance. 
     &lt;Application Target of Present Technology&gt; 
     Furthermore, although the image processing apparatus that performs image clustering has been described above as an application example of the present technology, the present technology can be applied to an arbitrary configuration. 
     For example, the present technology can be applied to various electronic devices such as a transmitter and a receiver (e.g., a television receiver and a mobile phone) for satellite broadcasting, cable broadcasting such as a cable TV, distribution on the Internet, and distribution to a terminal by cellular communication, or an apparatus (e.g., a hard disk recorder and a camera) that records an image in a medium such as an optical disk, a magnetic disk, a flash memory or the like, or plays back an image from these storage media. 
     Furthermore, for example, the present technology can be implemented as part of components of an apparatus such as a processor (e.g., a video processor) such as a system large scale integration (LSI) or the like, a module (e.g., video module) that uses a plurality of processors or the like, a unit (e.g., video unit) that uses a plurality of modules or the like, or a set (e.g., video set) that is obtained by further adding other functions to the unit. 
     Furthermore, for example, the present technology can also be applied to a network system, too, including a plurality of apparatuses. For example, the present technology can be implemented as cloud computing shared and processed in cooperation by a plurality of apparatuses via a network. For example, the present technology may be implemented for a cloud service that provides a service related to an image (moving image) to an arbitrary terminal such as a computer, an audio visual (AV) device, a portable information processing terminal, or an Internet of Things (IoT) device. 
     Note that, in this description, a system means a set of a plurality of components (e.g., apparatuses and modules (parts)), and whether or not all components are in the same housing does not matter. Therefore, each of a plurality of apparatuses housed in separate housings and connected via a network, and one apparatus in which a plurality of modules is housed in one housing is the system. 
     &lt;Field/Usage to which Present Technology is Applicable&gt; 
     Systems, apparatuses, processing units, and the like to which the present technology is applied can be used in arbitrary fields such as traffic, medical, crime prevention, agricultural, livestock industry, mining, beauty care, factory, home electric appliances, meteorological, and natural monitoring fields. Furthermore, usages of these systems, apparatuses, and processing units are also arbitrary. 
     &lt;Others&gt; 
     The embodiments of the present technology are not limited to the above-described embodiments, and various modifications can be made without departing from the gist of the present technology. 
     For example, a configuration described as one apparatus (or processing unit) may be divided and configured as a plurality of apparatuses (or processing units). Conversely, the configurations described above as a plurality of apparatuses (or processing units) may be collectively configured as one apparatus (or processing unit). Furthermore, a configuration other than the above-described configuration may be naturally added to the configuration of each apparatus (or each processing unit). Furthermore, as long as the configuration and the operation of the entire system are substantially the same, part of components of a certain apparatus (or processing unit) may be included in the components of another apparatus (or another processing unit). 
     Furthermore, for example, the above-described program may be executed by an arbitrary apparatus. In this case, this apparatus is only required to have necessary functions (functional blocks or the like), and be able to have obtain necessary information. 
     Furthermore, for example, each step of one flowchart may be executed by one apparatus, or may be shared and executed by a plurality of apparatuses. Furthermore, in a case where a plurality of processing is included in one step, a plurality of processing may be executed by one apparatus, or may be shared and executed by a plurality of apparatuses. In other words, a plurality of processing included in one step can be also executed as processing of a plurality of steps. Conversely, the processing described as a plurality of steps can be also collectively executed as one step. 
     Furthermore, according to, for example, a program executed by the computer, processing in steps that describe the program may be executed in chronological order according to the order described in this description, or may be executed in parallel or individually at necessary timing such as a time when invoked or the like. That is, unless a contradiction arises, processing in each step may be executed in an order different from the above-described order. Furthermore, the processing in steps that describe this program may be executed in parallel with processing of another program, or may be executed in combination with the processing of the another program. 
     Furthermore, for example, a plurality of techniques related to the present technology can be implemented independently alone unless a contradiction arises. Naturally, a plurality of arbitrary present technologies can be also implemented in combination. For example, part or entirety of the present technology described in one of the embodiments can be implemented in combination with part or entirety of the present technology described in other embodiments. Furthermore, part or entirety of the above-described arbitrary present technology can be implemented in combination with other technologies that are not described above. 
     Note that the present technology can also employ the following configurations. 
     (1) An image processing apparatus including: 
     a clustering unit configured to cluster a sparse pixel included in an image; and 
     an interpolation processing unit configured to interpolate sparse information by image filtering, and thereby derive a dense clustering result, the sparse information being obtained by the clustering of the clustering unit, and the image filtering using an image signal as a guide. 
     (2) The image processing apparatus according to (1), in which the sparse information is a model coefficient or a clustering result obtained by the clustering. 
     (3) The image processing apparatus according to (1) or (2), further including a sampling pixel selection unit configured to select a sparse sampling pixel from the image, 
     in which the clustering unit performs the clustering on the sparse sampling pixel selected by the sampling pixel selection unit. 
     (4) The image processing apparatus according to (3), in which the sampling pixel selection unit selects the sampling pixel from a portion included in a processing target area of the image on the basis of information regarding the processing target area. 
     (5) The image processing apparatus according to (3) or (4), in which 
     the image is a stitching image obtained by stitching a plurality of images, and 
     the sampling pixel selection unit selects the sampling pixel on the basis of stitching information that is information regarding the plurality of images of the stitching image overlapping each other. 
     (6) The image processing apparatus according to any one of (3) to (5), in which the sampling pixel selection unit selects the sampling pixel from a flat area of the image on the basis of information regarding the flat area. 
     (7) The image processing apparatus according to any one of (1) to (6), in which 
     the clustering unit performs local clustering by using sparse information as the clustering, the local clustering being clustering of a sparse pixel included in a local area of the image, and the sparse information being obtained by wide area clustering that is clustering of a sparse pixel included in a wide area of the image, and 
     the interpolation processing unit interpolates, by the image filtering, the sparse information obtained by the local clustering, and thereby derives a dense clustering result of the local area. 
     (8) The image processing apparatus according to (7), in which the sparse information obtained by the wide area clustering is a model coefficient or a clustering result. 
     (9) The image processing apparatus according to (7) or (8), in which the clustering unit further performs the local clustering on a processing target local area by using the sparse information obtained by the local clustering on one previous processing target local area. 
     (10) The image processing apparatus according to any one of (7) to (9), further including a sampling pixel selection unit configured to select a sparse sampling pixel from the local area, 
     in which the clustering unit performs the local clustering on the sparse sampling pixel selected by the sampling pixel selection unit. 
     (11) The image processing apparatus according to (10), the sampling pixel selection unit selects the sampling pixel from pixels in the local area other than pixels on which the wide area clustering has been performed. 
     (12) The image processing apparatus according to any one of (7) to (11), further including a wide area clustering unit configured to perform the wide area clustering, 
     in which the clustering unit performs the local clustering by using information obtained by the wide area clustering performed by the wide area clustering unit. 
     (13) An image processing method including: 
     clustering a sparse pixel included in an image; and 
     interpolating sparse information by image filtering, and thereby deriving a dense clustering result, the sparse information being obtained by the clustering, and the image filtering using an image signal as a guide. 
     (14) An image processing apparatus including a clustering unit configured to perform local clustering by using information, the local clustering being clustering of a dense pixel included in a local area of an image, and the information being obtained by wide area clustering that is clustering of a sparse pixel included in a wide area of the image. 
     (15) An image processing method including performing local clustering by using information, the local clustering being clustering of a dense pixel included in a local area of an image, and the information being obtained by wide area clustering that is clustering of a sparse pixel included in a wide area of the image. 
     REFERENCE SIGNS LIST 
     
         
           100  Image processing apparatus 
           111  Sampling pixel selection unit 
           112  Clustering unit 
           113  Interpolation processing unit 
           201  Field area storage unit 
           231  Stitching information storage unit 
           261  Flat area storage unit 
           300  Image processing apparatus 
           311  Prior learning unit 
           312  Additional learning unit 
           313  Coefficient storage unit 
           321  Sampling pixel selection unit 
           322  Clustering unit 
           351  Sampling pixel storage unit 
           400  Image processing apparatus 
           411  Sampling pixel selection unit 
           412  Clustering unit 
           413  Optimization unit 
           500  Image processing apparatus 
           511  Clustering unit 
           512  Vegetation area determination unit 
           700  Image processing apparatus 
           711  Clustering unit 
           712  Analysis unit 
           900  Computer