Patent Abstract:
An image processing apparatus, method and non-transitory computer program storage device cooperate to process successive images. Respective frames are created and positioned within the successive images, where each frame has a border. When changes between the frame borders are detected, a controller triggers the capturing of an image. This approach results in the capturing of interesting moments, even if the subject is not a human subject. The change in frame boundaries may be categorized in a variety of ways, including change in aspect ratio, shape, orientation, and position, for example. By detecting the changes in this way, an imaging device can capture images of interesting events automatically.

Full Description:
CROSS REFERENCE TO RELATED APPLICATION 
     The present application is a continuation of U.S. application Ser. No. 13/636,203, filed on Oct. 15, 2012, which is the National Stage of International Application No. PCT/JP2011/001547, filed on Mar. 16, 2011, and which claimed priority to Japanese Application No. 2010-079189, filed on Mar. 30, 2010. Each of the above-listed documents is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present invention relates to an image processing apparatus, method, and a computer program storage device. The present invention specifically relates to an image processing apparatus, method, and computer program storage device that are capable of obtaining a best shot image. 
     BACKGROUND ART 
     Recently, in imaging apparatuses such as a digital still camera, a technology has been proposed in which a facial expression detection function is provided that detects a face of a subject person and detects the expression of the face. When the facial expression detection function detects that the facial expression of the subject is a smile, a captured image is automatically recorded (refer to Patent Literature 1). 
     CITATION LIST 
     Patent Literature 
     
         
         [PTL 1] 
         Japanese Patent No. 4197019 
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     However, as recognized by the present inventors, with the technology described in Patent Literature 1, the triggering of the shutter is based only the expression of the face, and a change in the state of the subject other than the face, such as the moment when a running person falls down, the moment when a child stops moving around, or the like, cannot be automatically recorded as a captured image. Further, the technology described in Patent Literature 1 cannot be applied to a subject having no facial expression, other than a person. 
     The present invention has been made in light of the foregoing circumstances, and particularly, the present invention aims to obtain a best shot image more reliably. 
     For example, an exemplary image processing apparatus according to one embodiment of the present invention includes
         a processor configured to create a first frame border positioned within a first image and a second frame border positioned within a second image, the first image and the second image being sequential images in time; and   a controller configured to detect a change between the first frame border and the second frame border.       

     The image processing apparatus optional includes a shutter, and a shutter triggering mechanism configured to actuate the shutter and capture an image with an image sensor in response to the controller detecting a change between the first frame border and the second frame border. The change between the first frame border and the second frame border may be at least one of
         a change in aspect ratio,   a change in shape, and   a change in position. Also, change between the first frame border and the second frame border may occur in response to one of a movement of a subject within the first frame border and second frame, and a feature change of the subject.       

     The shutter triggering mechanism may be configured to actuate the shutter after a predetermined period of time in which the shutter is inactive. 
     This exemplary image processing apparatus may process the first image and the second image within a video, wherein the video including images captured in a viewfinder of at least one of a digital still camera and a digital video recorder; and the first frame border and the second frame border being visible within the viewfinder. 
     Additionally, the processor is configured to determine a first smaller frame positioned within the first frame border, and a second smaller frame within the second frame border, and
         the change between the first frame border and second frame border is detected by the controller when a ratio of areas of the first smaller frame to first frame border and a ratio of areas of the second smaller frame to second frame border satisfies a predetermined criteria.       

     Another exemplary embodiment of the present invention is a method that includes
         determining with a processor a first frame border positioned within a first image and a second frame border positioned within second image, the first image and the second image being sequential images in time; and   detecting a change between the first frame border and the second frame border.       

     This method optional actuates a shutter and captures an image with an image sensor in response to the detecting a change between the first frame border and the second frame border. The change between the first frame border and the second frame border being at least one of
         a change in aspect ratio,   a change in shape, and   a change in position. Also, the change between the first frame border and the second frame border occurs in response to one of a movement of a subject within the first frame border and second frame, and a feature change of the subject.       

     The shutter may be actuated after a predetermined period of time in which the shutter is inactive. 
     The method may also include capturing the images in a viewfinder of at least one of a digital still camera and a digital video recorder; and
         presenting the first frame border and the second frame border within the viewfinder.       

     Optionally, the method may determine a first smaller frame positioned within the first frame border, and a second smaller frame within the second frame border, wherein a change between the first frame border and second frame border is detected when a ratio of areas of the first smaller frame to first frame border and a ratio of areas of the second smaller frame to second frame border satisfies a predetermined criteria. 
     Another exemplary embodiment of the present invention is a non-transitory computer readable storage device having instructions that when executed by a processor perform a method including
         determining with a processor a first frame border positioned within the first image and a second frame border positioned within the second image, the first image and the second image being sequential images in time; and   detecting a change between the first frame border and the second frame border.       

     The non-transitory computer program storage device may also actuate a shutter and capture an image with an image sensor in response to the detecting a change between the first frame border and the second frame border, wherein
         the change between the first frame border and the second frame border being at least one of
           a change in aspect ratio,   a change in shape, and   a change in position. The images may be captured in a viewfinder of at least one of a digital still camera and a digital video recorder in which the first frame border and the second frame border are presented within the viewfinder.   
               

     Another feature that may be employed is the determination of a first smaller frame positioned within the first image, and a second smaller frame within the second image, wherein the detecting includes detecting a change of the first frame border and second frame border when a ratio of areas between the first smaller frame to first frame border and a ratio of areas of the second smaller frame to second frame border satisfies a predetermined criteria. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram showing an example of a configuration of an image processing apparatus according to an embodiment of the present invention. 
         FIG. 2  is a block diagram showing an example of a configuration of a subject tracking unit. 
         FIG. 3  is a block diagram showing an example of a configuration of a subject map generation unit. 
         FIG. 4  is a block diagram showing an example of a configuration of a subject candidate area rectangle forming unit. 
         FIG. 5  is a block diagram showing an example of a configuration of a subject area selection unit. 
         FIG. 6  is a flowchart illustrating subject tracking processing. 
         FIG. 7  is a flowchart illustrating subject map generation processing. 
         FIG. 8  is a diagram showing a specific example of the subject map generation processing. 
         FIG. 9  is a flowchart illustrating subject candidate area rectangle forming processing. 
         FIG. 10  is a diagram showing a specific example of the subject candidate area rectangle forming processing. 
         FIG. 11  is a flowchart illustrating subject area selection processing. 
         FIG. 12  is a diagram illustrating a sum of subject area feature quantities of a band saliency map. 
         FIG. 13  is a diagram illustrating weighting factors. 
         FIG. 14  is a block diagram showing an example of a functional configuration of a control unit. 
         FIG. 15  is a flowchart illustrating automatic shutter processing. 
         FIG. 16  is a diagram illustrating a change in the aspect ratio of the subject area. 
         FIG. 17  is a block diagram showing another example of the functional configuration of the control unit. 
         FIG. 18  is a flowchart illustrating automatic shutter processing. 
         FIG. 19  is a block diagram showing yet another example of the functional configuration of the control unit. 
         FIG. 20  is a flowchart illustrating automatic shutter processing. 
         FIG. 21  is a diagram illustrating a change in the aspect ratio of the subject area within a predetermined area. 
         FIG. 22  is a block diagram showing another example of the configuration of the image processing apparatus. 
         FIG. 23  is a block diagram showing an example of a functional configuration of a control unit shown in  FIG. 22 . 
         FIG. 24  is a flowchart illustrating automatic shutter processing. 
         FIG. 25  is a diagram illustrating a change in the ratio of the subject area and the face area. 
         FIG. 26  is a block diagram showing another example of the functional configuration of the control unit. 
         FIG. 27  is a flowchart illustrating automatic shutter processing. 
         FIG. 28  is a diagram illustrating a change in the ratio of the subject area and the face area. 
         FIG. 29  is a block diagram showing yet another example of the configuration of the image processing apparatus. 
         FIG. 30  is a block diagram showing an example of a functional configuration of a control unit shown in  FIG. 29 . 
         FIG. 31  is a flowchart illustrating frame identification processing. 
         FIG. 32  is a block diagram showing an example of a hardware configuration of a computer. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, an embodiment of the present invention will be explained with reference to the drawings. 
     (Example of Configuration of Image Processing Apparatus) 
       FIG. 1  is a diagram showing an example of a configuration of an image processing apparatus  11  according to the embodiment of the present invention. 
     The image processing apparatus  11  is provided in an imaging apparatus, such as a digital video camera that captures an image of a moving subject and a digital still camera, for example. 
     The image processing apparatus  11  includes an optical system  31 , an imager  32 , a digital signal processing unit  33 , a display unit  34 , a control unit  35 , a lens drive unit  36 , an interface control unit  37  and a user interface  38 . 
     The optical system  31  is formed as an optical system that includes an imaging lens (not shown in the drawings). The light entering the optical system  31  is photoelectrically converted by the imager  32  that is formed by imaging elements such as charge coupled devices (CCDs). An electric signal (an analog signal) that has been photoelectrically converted by the imager  32  is converted into image data of a digital signal by an analog to digital (A/D) conversion unit (not shown in the drawings), and the image data is supplied to the digital signal processing unit  33 . 
     The digital signal processing unit  33  performs predetermined signal processing on the image data supplied from the imager  32 . The digital signal processing unit  33  includes a pre-processing unit  51 , a demosaic processing unit  52 , a YC generation unit  53 , a resolution conversion unit  54 , a subject tracking unit  55  and a CODEC  56 . 
     The pre-processing unit  51  performs, as pre-processing, on the image data from the imager  32 , clamp processing that clamps a black level of R, G and B to a predetermined level, correction processing between color channels of R, G and B, and the like. The demosaic processing unit  52  performs, on the image data that has been pre-processed by the pre-processing unit  51 , demosaic processing that interpolates color components of pixels so that each pixel of the image data has all color components of R, G and B. 
     The YC generation unit  53  generates (separates) a luminance (Y) signal and a color (C) signal, from the image data of R, G and B that has been subject to demosaic processing by the demosaic processing unit  52 . The resolution conversion unit  54  performs resolution conversion processing on the image data processed by the YC generation unit  53 . 
     The subject tracking unit  55  performs subject tracking processing. The subject tracking processing detects, based on the image data formed by the luminance signal and the color signal generated by the YC generation unit  53 , a subject in an input image corresponding to the image data and tracks the subject. 
     Here, the detection of the subject is performed on the assumption that the subject is an object in the input image that is assumed to attract a user&#39;s attention when the user glances at the input image, namely, an object that is assumed to be looked at by the user. Therefore, the subject is not limited to a person. 
     The subject tracking unit  55  supplies, to the control unit  35 , data about a subject frame obtained as a result of the subject tracking processing. The subject frame indicates an area in the input image, the area including the subject. Note that the subject tracking unit  55  will be described in more detail later with reference to  FIG. 2 . 
     The CODEC  56  encodes the image data generated by the YC generation unit  53  or the resolution conversion unit  54  and the image data recorded in a DRAM  40 , if necessary. Further, the CODEC  56  records the encoded image data in a recording medium (not shown in the drawings) or decodes the encoded image data. The image data decoded by the CODEC  56  or the image data obtained by the resolution conversion unit  54  is supplied to the display unit  34  and is displayed thereon. The display unit  34  is formed by a liquid crystal display, for example. The display unit  34  displays an input image that corresponds to the image data supplied from the digital signal processing unit  33  in accordance with control by the control unit  35 . 
     The control unit  35  controls each unit of the image processing apparatus  11  in accordance with a control signal supplied from the interface control unit  37   
     For example, the control unit  35  supplies to the digital signal processing unit  33  parameters and the like that are used for various types of signal processing. Further, the control unit  35  acquires data obtained as a result of the various types of signal processing from the digital signal processing unit  33 , and supplies the data to the interface control unit  37 . 
     Further, the control unit  35  causes display of the subject frame on the input image displayed on the display unit  34 , based on the data about the subject frame supplied from the subject tracking unit  55 . The subject frame indicates an area in the input image, the area including the subject. 
     Further, the control unit  35  drives the imaging lens included in the optical system  31 , and supplies a control signal to the lens drive unit  36  to adjust the aperture or the like. Furthermore, the control unit  35  controls capture of an input image by the imager  32 . 
     The user interface  38  includes input devices, such as a button, a lever, a switch, a microphone and the like that are operated when the user inputs a command to the image processing apparatus  11 . Further, the user interface  38  includes output devices, such as a lamp, a speaker and the like that present information to the user. 
     For example, when the button as the user interface  38  is operated, the user interface  38  supplies a control signal in accordance with the operation to the control unit  35  via the interface control unit  37 . 
     (Example of Configuration of Subject Tracking Unit) 
     Next, an example of a configuration of the subject tracking unit  55  shown in  FIG. 1  will be explained with reference to  FIG. 2 . 
     The subject tracking unit  55  shown in  FIG. 2  includes a subject map generation unit  71 , a subject candidate area rectangle forming unit  72 , a subject area selection unit  73 , and a weighting factor calculation unit  74 . 
     The subject map generation unit  71  generates, for each feature of the input image such as luminance and color, a saliency map that indicates a feature quantity in a predetermined area of a predetermined frame of the input image, and supplies the generated saliency map to the weighting factor calculation unit  74 . Further, the subject map generation unit  71  generates a subject map that indicates a likelihood of an area including a subject in the input image, based on the generated saliency map and a weighting factor for each feature quantity supplied from the weighting factor calculation unit  74 . 
     More specifically, the subject map generation unit  71  performs weighted addition of information (feature quantity) of each area of the saliency map generated for each feature, and thereby generates the subject map. The weighted addition is performed for each area in the same position. The subject map generation unit  71  supplies the generated subject map to the subject candidate area rectangle forming unit  72 . 
     Note that, in each saliency map, an area with a larger amount of information, namely, an area in the input image corresponding to an area with a large feature quantity is an area with a higher possibility of including a subject. Accordingly, based on each saliency map, it is possible to identify, in the input image, the area that includes the subject. 
     In the subject map supplied from the subject map generation unit  71 , the subject candidate area rectangle forming unit  72  obtains an area to be a subject candidate, namely, a rectangular area including the area with a large amount of information in the subject map, and supplies coordinate information indicating coordinates of the rectangular area to the subject area selection unit  73 . Further, the subject candidate area rectangle forming unit  72  calculates information relating to the rectangular area (hereinafter referred to as area information) indicated by the coordinate information on the subject map, associates the area information with the coordinate information, and supplies it to the subject area selection unit  73 . 
     Based on the area information supplied from the subject candidate area rectangle forming unit  72 , the subject area selection unit  73  selects, from the rectangular area, a subject area that is a rectangular area including a subject of interest, which is a tracking target. Then, the subject area selection unit  73  supplies coordinate information of the subject area to the control unit  35  (refer to  FIG. 1 ) and the weighting factor calculation unit  74 . 
     The weighting factor calculation unit  74  calculates a weighting factor used to weight the saliency map of the next frame that corresponds to a relatively large feature quantity, among the feature quantities in the area corresponding to the subject area on each quantity feature map of a predetermined frame supplied from the subject map generation unit  71 . Then, the weighting factor calculation unit  74  supplies the calculated weighting factor to the subject map generation unit  71 . 
     With the above-described configuration, the subject tracking unit  55  can obtain the subject frame indicating the subject area, for each frame of the input image. 
     (Example of Configuration of Subject Map Generation Unit) 
     Next, an example of a configuration of the subject map generation unit  71  shown in  FIG. 2  will be explained with reference to  FIG. 3 . 
     As shown in  FIG. 3 , the subject map generation unit  71  includes a saliency map generation unit  111 , a band saliency map generation unit  112 , a band saliency map synthesis unit  113  and a synthesized saliency map synthesis unit  114 . 
     From a predetermined frame of the input image, the saliency map generation unit  111  generates, for each feature quantity, a saliency map that indicates information (feature quantity) relating to features such as luminance and color, and supplies the generated saliency map to the band saliency map generation unit  112 . 
     The band saliency map generation unit  112  extracts a feature quantity of a predetermined band component a predetermined number of times, from the feature quantity in each saliency map supplied from the saliency map generation unit  111 , and generates band saliency maps that indicate each extracted feature quantity. Then, the band saliency map generation unit  112  supplies the generated band saliency maps to the weighting factor calculation unit  74  and the band saliency map synthesis unit  113 . 
     The band saliency map synthesis unit  113  synthesizes, for each feature quantity, the band saliency maps supplied from the band saliency map generation unit  112 , based on the weighting factor supplied from the weighting factor calculation unit  74 , and thereby generates synthesized saliency maps. Then, the band saliency map synthesis unit  113  supplies the synthesized saliency maps to the weighting factor calculation unit  74  and the synthesized saliency map synthesis unit  114 . 
     The synthesized saliency map synthesis unit  114  synthesizes the synthesized saliency maps supplied from the band saliency map synthesis unit  113 , based on the weighting factors supplied from the weighting factor calculation unit  74 , and thereby generates a subject map. Then, the synthesized saliency map synthesis unit  114  supplies the subject map to the subject candidate area rectangle forming unit  72  (refer to  FIG. 2 ). 
     Hereinafter, the band saliency map and the synthesized saliency map that are described above are also simply referred to as a saliency map. 
     (Example of Configuration of Subject Candidate Area Rectangle Forming Unit) 
     Next, an example of a configuration of the subject candidate area rectangle forming unit  72  shown in  FIG. 2  will be explained with reference to  FIG. 4 . 
     As shown in  FIG. 4 , the subject candidate area rectangle forming unit  72  includes a binarization processing unit  131 , a labeling processing unit  132 , a rectangular area coordinate calculation unit  133  and an area information calculation unit  134 . 
     The binarization processing unit  131  binarizes information, which corresponds to each pixel of the input image in the subject map supplied from the subject map generation unit  71 , to a value of 0 or 1 based on a predetermined threshold value, and supplies the value to the labeling processing unit  132 . Hereinafter, the information that corresponds to each pixel of the input image in the subject map is also simply referred to as a pixel. 
     In the binarized subject map supplied from the binarization processing unit  131 , the labeling processing unit  132  labels an area in which pixels whose value is 1 are adjacent to each other (hereinafter, the area is referred to as a connected area), and supplies the subject map with the labeled connected area to the rectangular area coordinate calculation unit  133 . 
     In the subject map having the labeled connected area supplied from the labeling processing unit  132 , the rectangular area coordinate calculation unit  133  calculates coordinates of a rectangular area including (surrounding) the connected area. Then, the rectangular area coordinate calculation unit  133  supplies coordinate information indicating the coordinates to the area information calculation unit  134  together with the subject map. 
     The area information calculation unit  134  calculates area information that is information relating to the rectangular area indicated by the coordinate information on the subject map supplied from the rectangular area coordinate calculation unit  133 . Then, the area information calculation unit  134  associates the area information with the coordinate information, and supplies it to the subject area selection unit  73  (refer to  FIG. 1 ). 
     (Example of Configuration of Subject Area Selection Unit) 
     Next, an example of a configuration of the subject area selection unit  73  will be explained with reference to  FIG. 5 . 
     As shown in  FIG. 5 , the subject area selection unit  73  includes an area information comparison unit  151  and a subject area decision unit  152 . 
     The area information comparison unit  151  compares the area information of each rectangular area supplied from the subject candidate area rectangle forming unit  72  with the area information of the subject area one frame before (e.g., sequential images in time), which is stored in an area information storage unit  153 , and supplies a comparison result to the subject area decision unit  152 . 
     Based on the comparison result supplied from the area information comparison unit  151 , the subject area decision unit  152  decides, as the subject area, the rectangular area indicated by the coordinate information associated with area information that is closest to the area information of the subject area one frame before. The subject area decision unit  152  supplies coordinate information of the decided subject area to the control unit  35  (refer to  FIG. 1 ) and the weighting factor calculation unit  74  (refer to  FIG. 2 ). At the same time, the subject area decision unit  152  supplies the area information of the subject area to the area information storage unit  153 . 
     The area information storage unit  153  stores the area information of the subject area supplied from the subject area decision unit  152 . The area information of the subject area stored in the area information storage unit  153  is read out after one frame by the area information comparison unit  151 . 
     (Subject Tracking Processing) 
     Hereinafter, the subject tracking processing of the image processing apparatus  11  will be explained. 
       FIG. 6  is a flowchart illustrating the subject tracking processing performed by the image processing apparatus  11 . The subject tracking processing is started, for example, when the operation mode of the image processing apparatus  11  is shifted to a subject tracking mode that performs the subject tracking processing, by the user operating a button as the user interface  38 , and a predetermined area of the subject as a tracking target is selected by the user in the input image displayed on the display unit  34 . 
     At step S 11 , the subject map generation unit  71  of the subject tracking unit  55  performs subject map generation processing and generates a subject map. The subject map generation unit  71  supplies the subject map to the subject candidate area rectangle forming unit  72 . 
     (Subject Map Generation Processing) 
     Here, with reference to  FIG. 7  and  FIG. 8 , the subject map generation processing will be explained in detail.  FIG. 7  is a flowchart illustrating the subject map generation processing, and  FIG. 8  is a diagram showing a specific example of the subject map generation processing. 
     At step S 31  of the flowchart shown in  FIG. 7 , the saliency map generation unit  111  of the subject map generation unit  71  generates a saliency map (for each feature quantity) for each of the features such as luminance and color, from a predetermined frame of an input image. Then, the saliency map generation unit  111  supplies the generated saliency maps to the band saliency map generation unit  112 . 
     More specifically, as shown in  FIG. 8 , M types of saliency maps are generated from an input image  200 . The M types of saliency maps include a luminance information map F 1  that indicates information relating to luminance, color information maps F 2  to FK that indicate information relating to color, and edge information maps F (K+1) to FM that indicate information relating to edge 
     In the luminance information map F 1 , a luminance component (a luminance signal) Y that is obtained from each pixel of the input image is taken as information corresponding to each pixel of the input image. In the color information maps F 2  to FK, color components (color signals) R, G and B obtained from each pixel of the input image are taken as information corresponding to each pixel of the input image. Further, in the edge information maps F (K+1) to FM, edge intensities in the directions of 0 degree, 45 degree, 90 degree and 135 degree in each pixel of the input image, for example, are taken as information corresponding to each pixel of the input image. 
     Note that, with respect to the above-described saliency maps, an average value of values of the respective components of R, G and B of the pixel may be used as information (feature quantity) of the luminance information map F 1 , and color difference components Cr and Cb, or an a * coordinate component and a b * coordinate component in a Lab color space may be used as information of the color information maps F 2  to FK. Further, edge intensities in directions other than the directions of 0 degree, 45 degree, 90 degree and 135 degree may be used as information of the edge information maps F (K+1) to FM. 
     At step S 32 , the band saliency map generation unit  112  extracts a feature quantity of a predetermined band component, N times, from the feature quantity in each saliency map, and generates band saliency maps that indicate each extracted feature quantity. Then, the band saliency map generation unit  112  supplies the generated band saliency maps to the weighting factor calculation unit  74  and the band saliency map synthesis unit  113 . 
     More specifically, as shown in  FIG. 8 , luminance information of band  1  to band N is extracted from luminance information in the luminance map F 1 , and band luminance information maps R 11  to R 1 N are generated that indicate luminance information of each of the bands. Further, color information of band  1  to band N is extracted from color information in the color information maps F 2  to FK, and band color information maps R 21  to R 2 N, . . . , RK 1  to RKN are generated that indicate color information of each of the bands. Further, edge information of band  1  to band N is extracted from edge information in the edge information maps F (K+1) to FM, and band edge information maps R (K+1)  1  to R (K+1) N, . . . , RM 1  to RMN are generated that indicate edge information of each of the bands. In this manner, the band saliency map generation unit  112  generates (M×N) types of band saliency map. 
     Here, an example of processing performed by the band saliency map generation unit  112  will be explained. 
     For example, the band saliency map generation unit  112  uses each saliency map to generate a plurality of saliency maps having resolutions different from each other, and represents the saliency maps as pyramid images of the corresponding feature quantity. For example, pyramid images in eight layers of resolution of level L 1  to level L 8  are generated. It is assumed that the pyramid image of level L 1  has the highest resolution and the resolutions of the pyramid images become lower in order from level L 1  to level L 8 . 
     In this case, the saliency map generated by the saliency map generation unit  111  is represented as the pyramid image of level L 1 . Further, an average value of pixel values of four pixels that are adjacent to each other in a pyramid image of level Li (where i=1 or i=7 or 1&lt;i&lt;7) is taken as a pixel value of one pixel of a pyramid image of level L (i+1) that corresponds to the adjacent four pixels. Accordingly, the pyramid image of level L (i+1) is a half image (rounded down if not divisible), in height and width, of the pyramid image of level Li. 
     Further, the band saliency map generation unit  112  selects two pyramid images in different layers from among the plurality of pyramid images, and obtains a difference between the selected pyramid images, thereby generating an N number of difference images of each feature quantity. Note that, since the pyramid images in the respective layers are different in size (different in number of pixels), at the time of the generation of a difference image, a smaller pyramid image is up-converted in accordance with the size of a larger image. 
     For example, among the pyramid images of feature quantities in the respective layers, the band saliency map generation unit  112  obtains a difference between the pyramid images in combinations of the respective layers of level L 6  and level L 3 , level L 7  and level L 3 , level L 7  and level L 4 , level L 8  and level L 4 , and level L 8  and level L 5 . Thus, difference images of a total of five feature quantities are obtained. 
     More specifically, for example, in a case where the difference image of the combination of level L 6  and level L 3  is generated, the pyramid image of level L 6  is up-converted in accordance with the size of the pyramid image of level L 3 . Namely, the pixel value of one pixel in the pyramid image of level L 6  before up-conversion is taken as the pixel value of some pixels adjacent to each other in the pyramid image of level L 6  after up-conversion. Then, a difference between the pixel value of the pixel in the pyramid image of level L 6  and the pixel value of the pixel in the pyramid image of level L 3  located in the same position as the pixel in the pyramid image of level L 6  is obtained, and the difference is taken as the pixel value of the pixel in the difference image. 
     By generating a difference image in this manner, it is possible to extract a feature quantity of a predetermined band component from the saliency map, as if filter processing using a band pass filter is applied to the saliency map. 
     Note that, in the above description, although the width of the band extracted from the saliency map is determined by the combination of the respective layers of pyramid images when the difference image is obtained, the combination can be decided as desired. 
     Further, the extraction of the feature quantity of a predetermined band component is not limited to the above-described technique using a difference image, and another technique may be used. 
     Returning to the flowchart in  FIG. 7 , at step S 33 , the band saliency map synthesis unit  113  synthesizes, for each feature quantity, the band saliency maps supplied from the band saliency map generation unit  112 , based on a group of weighting factors WR supplied from the weighting factor calculation unit  74 . The band saliency map synthesis unit  113  supplies the synthesized band saliency maps (synthesized saliency maps) to the weighting factor calculation unit  74  and the synthesized saliency map synthesis unit  114 . 
     More specifically, as shown in  FIG. 8 , weighted addition of the band luminance information maps R 11  to R 1 N is performed using weighting factors w 11  to w 1 N that are weights for each of the band luminance information maps supplied from the weighting factor calculation unit  74 , and a synthesized saliency map C 1  is obtained. Further, weighted addition of the band color information maps R 21  to R 2 N, RK 1  to RKN is performed using weighting factors w 21  to w 2 N, . . . , wK 1  to wKN that are weights for each of the band color information maps supplied from the weighting factor calculation unit  74 , and synthesized saliency maps C 2  to CK are obtained. Further, weighted addition of the band edge information maps R (K+1)  1  to R (K+1) N . . . , RM 1  to RMN is performed using weighting factors w (K+1)  1  to w (K+1) N, . . . wM 1  to wMN that are weights for each of the band edge information maps supplied from the weighting factor calculation unit  74 , and synthesized saliency maps CK+1 to CM are obtained. In this manner, the band saliency map synthesis unit  113  generates M types of synthesized saliency map. Note that, although the group of weighting factors WR will be described in more detail later, the respective weighting factors of the group of weighting factors WR have a value from 0 to 1. However, when the subject map generation processing is performed for the first time, the respective weighting factors of the group of weighting factors WR are all set to 1, and the band saliency maps are added without weight. 
     At step S 34 , the synthesized saliency map synthesis unit  114  synthesizes the synthesized saliency maps supplied from the band saliency map synthesis unit  113 , based on a group of weighting factors WC supplied from the weighting factor calculation unit  74 , and thereby generates a subject map and supplies the subject map to the subject candidate area rectangle forming unit  72 . 
     More specifically, as shown in  FIG. 8 , the synthesized saliency maps C 1  to CM are linearly coupled using weighting factors w 1  to wM that are weights for each of the band luminance information maps supplied from the weighting factor calculation unit  74 . Further, the pixel value of the map obtained as a result of the linear coupling is multiplied by a subject weight, which is a weight obtained in advance, and is normalized, thereby obtaining a subject map  201 . Note that, although the group of weighting factors WC will be described in more detail later, the respective weighting factors of the group of weighting factors WC have a value from 0 to 1. Note, however, that when the subject map generation processing is performed for the first time, the respective weighting factors of the group of weighting factors WC are all set to 1, and the synthesized saliency maps are linearly coupled without weight. 
     In other words, if a position (pixel) of interest on the subject map to be obtained is taken as a target position, the pixel value of the same position (pixel) as the target position on each of the synthesized saliency maps is multiplied by the weighting factor for each of the synthesized saliency maps, and a sum of the pixel values multiplied by the weighting factors is taken as the pixel value of the target position. Further, the pixel value of each position on the subject map obtained in this manner is multiplied by the subject weight, which has been obtained in advance for the subject map, and is normalized, thereby obtaining a final subject map. For example, normalization is performed such that the pixel value of each pixel of the subject map is a value from 0 to 255. 
     In the manner described above, the subject map generation unit  71  generates the band saliency maps and the synthesized saliency maps, from the saliency maps, and thereby generates the subject map. 
     Returning to the flowchart in  FIG. 6 , at step S 12 , the subject candidate area rectangle forming unit  72  performs subject candidate area rectangle forming processing, and obtains a rectangular area including an area to be a subject candidate, in the subject map supplied from the subject map generation unit  71 . 
     (Subject Candidate Area Rectangle Forming Processing) 
     The subject candidate area rectangle forming processing will now be explained in detail with reference to  FIG. 9  and  FIG. 10 .  FIG. 9  is a flowchart illustrating the subject candidate area rectangle forming processing, and  FIG. 10  is a diagram showing a specific example of the subject candidate area rectangle forming processing. 
     At step S 51  of the flowchart shown in  FIG. 9 , the binarization processing unit  131  of the subject candidate area rectangle forming unit  72  binarizes information in the subject map supplied from the subject map generation unit  71  to one of the values 0 and 1 based on a predetermined threshold value, and supplies the values to the labeling processing unit  132 . 
     More specifically, with respect to the pixel value (which is a value from 0 to 255) of each of the pixels in the subject map  201  shown at the top of  FIG. 10 , the binarization processing unit  131  sets 0 as a pixel value that is smaller than a threshold value 127, and sets 1 as a pixel value that is equal to or larger than the threshold value 127. Thus, a binarized map  202  is obtained, an example of which is shown second from the top of  FIG. 10 . In the binarized map  202  shown in  FIG. 10 , a section (pixels) shown in white has the pixel value of 1, and a section (pixels) shown in black has the pixel value of 0. Note that, although it is assumed here that the threshold value is 127, it may be another value. 
     At step S 52 , in the binarized map  202  (the binarized subject map) supplied from the binarization processing unit  131 , the labeling processing unit  132  performs labeling on a connected area in which the pixels whose pixel value is 1 are adjacent to each other, which is obtained by a morphological operation, for example. Then, the labeling processing unit  132  supplies the binarized map  202  to the rectangular area coordinate calculation unit  133 . 
     More specifically, for example, as shown by the third map from the top in  FIG. 10 , in the binarized map  202 , a connected area  211  is labeled by a label “1” and a connected area  212  is labeled by a label “2”. 
     At step S 53 , in the binarized map  202  supplied from the labeling processing unit  132 , the rectangular area coordinate calculation unit  133  calculates coordinates of rectangular areas respectively including (surrounding) the connected areas  211  and  212 . Then, the rectangular area coordinate calculation unit  133  supplies coordinate information indicating the coordinates of the rectangular areas to the area information calculation unit  134  together with the binarized map  202 . 
     More specifically, as shown by the fourth map from the top in  FIG. 10 , in the binarized map  202 , a rectangular frame (a circumscribing frame)  221  that outwardly surrounds the connected area  211  labeled by the label “1” is detected, and coordinates of the upper left vertex and the lower right vertex in the drawing, for example, of the rectangular frame  221  are obtained. Further, a rectangular frame  222  that outwardly surrounds the connected area  212  labeled by the label “2” is detected, and coordinates of the upper left vertex and the lower right vertex in the drawing, for example, of the rectangular frame  222  are obtained. 
     At step S 54 , the area information calculation unit  134  calculates area information about the rectangular areas surrounded by the rectangular frames on the subject map, based on the coordinate information supplied from the rectangular area coordinate calculation unit  133  and the subject map supplied from the subject map generation unit  71 . 
     More specifically, based on the coordinate information supplied from the rectangular area coordinate calculation unit  133 , which indicates the rectangular frames  221  and  222  in the binarized map  202 , the area information calculation unit  134  calculates the size of each of the rectangular frames  221  and  222  and coordinates of the center position of each of the rectangular frames  221  and  222  as area information about each rectangular area. The area information calculation unit  134  associates the calculated area information with the coordinate information supplied from the rectangular area coordinate calculation unit  133 , and supplies the associated area information to the subject area selection unit  73 . 
     In the manner described above, the subject candidate area rectangle forming unit  72  obtains, in the subject map, the rectangular frames that surround each area to be a candidate for the subject of interest, and the area information indicating the feature of the areas surrounded by the rectangular frames on the subject map. The rectangular frames are defined by a border positioned within a boundary of the image in which it is disposed. 
     Returning to the flowchart in  FIG. 6 , at step S 13 , the subject area selection unit  73  performs subject area selection processing, and selects a subject area that is a rectangular area including the subject of interest, from among the rectangular areas, based on the area information supplied from the subject area selection unit  73 . 
     (Subject Area Selection Processing) 
     Here, with reference to a flowchart in  FIG. 11 , the subject area selection processing will be explained in more detail. 
     At step S 71 , the area information comparison unit  151  compares the area information of each rectangular area, which is supplied from the subject candidate area rectangle forming unit  72 , with the area information of the subject area one frame before, which is stored in the area information storage unit  153 , and supplies a comparison result to the subject area decision unit  152 . 
     More specifically, for example, the area information comparison unit  151  compares the size of the rectangular frame that surrounds each rectangular area on the subject map, which is supplied from the subject candidate area rectangle forming unit  72 , with the size of the rectangular frame (the subject frame) that surrounds the subject area one frame before, which is stored in the area information storage unit  153 . While area of the frame border is one featured that can be detected, other relative attributes of the frame may be detected between successive frames, such as position, shape and aspect ratio. Further, for example, the area information comparison unit  151  compares the coordinates of the center position of the rectangular frame that surrounds each rectangular area on the subject map, which are supplied from the subject candidate area rectangle forming unit  72 , with the coordinates of the center position of the rectangular frame (the subject frame) that surrounds the subject area one frame before, which are stored in the area information storage unit  153 . 
     At step S 72 , based on the comparison result from the area information comparison unit  151 , the subject area decision unit  152  decides, as the subject area, one of a rectangular area having the size of the rectangular frame (the subject frame) that surrounds the subject area one frame before, a rectangular area having the size of the rectangular frame that is closest to the coordinates of the center position, and a rectangular area including the center position. The subject area decision unit  152  supplies coordinate information of the decided subject area to the control unit  35  and the weighting factor calculation unit  74 . At the same time, the subject area decision unit  152  supplies area information (the size or the center position of the subject frame) of the decided subject area to the area information storage unit  153 . 
     Note that, when the subject area selection processing is performed for the first time, the area information of the subject area one frame before is not stored in the area information storage unit  153 . Therefore, the rectangular area including a predetermined area of the subject selected by the user at the time of the start of the subject tracking processing (hereinafter, the predetermined area is referred to as an initially selected area) is set as the subject area. 
     In the manner described above, the subject area selection unit  73  selects the subject area of the subject of interest, from the rectangular areas that are subject candidates. 
     (Calculation of Weighting Factors) 
     Returning to the flowchart in  FIG. 6 , at step S 14 , the weighting factor calculation unit  74  calculates the group of weighting factors WR and the group of weighting factors WC shown in  FIG. 8 , based on the band saliency map and the synthesized saliency map supplied from the subject map generation unit  71 , and on the coordinate information indicating the subject area supplied from the subject area selection unit  73 . 
     More specifically, as shown in  FIG. 12 , if a sum of feature quantities (information quantities) in a rectangular area corresponding to a subject frame  231  that represents the subject area on a predetermined band saliency map Rmn (1=m or 1&lt;m&lt;M or m=M, 1=n or 1&lt;n&lt;N or n=N) is taken as a sum rmn of subject area feature quantities, the group of weighting factors WR shown in the upper section of  FIG. 13  is calculated. 
     The respective factors in the group of weighting factors WR shown in  FIG. 13  correspond to the respective weighting factors w 11  to wMN shown in  FIG. 8 . Note that, in  FIG. 13 , Max (a, . . . , z) indicates the maximum value among the values a to z. 
     For example, the respective factors in the first row from the top in the group of weighting factors WR shown in  FIG. 13  indicate the weighting factors w 11  to wM 1  with respect to band saliency maps R 11  to RM 1  for each feature quantity corresponding to “band  1 ” shown in  FIG. 8 . As shown in  FIG. 13 , the weighting factors w 11  to wM 1  are factors that take a value from 0 to 1 such that their denominators are maximum values among sums r 11  to rM 1  of subject area feature quantities for the respective band saliency maps R 11  to RM 1 , and their numerators are the sums r 11  to rM 1  of the subject area feature quantities for the respective band saliency maps R 11  to RM 1 . 
     In a similar manner, the respective factors in the N-th row from the top in the group of weighting factors WR shown in  FIG. 13  indicate the weighting factors w 1 N to wMN with respect to band saliency maps R 1 N to RMN for each feature quantity corresponding to “band N” shown in  FIG. 8 . As shown in  FIG. 13 , the weighting factors w 1 N to wMN are factors that take a value from 0 to 1 such that their denominators are maximum values among sums r 1 N to rMN of subject area feature quantities for the respective band saliency maps R 1 N to RMN, and their numerators are the sums r 1 N to rMN of the subject area feature quantities for the respective band saliency maps R 1 N to RMN. 
     In other words, according to the weighting factors w 1   n  to wMn, among the band saliency maps R 1   n  to RMn for each feature quantity corresponding to “band n”, weighting is performed such that the maximum value becomes 1 for the band saliency map of the feature quantity in which the sum of the subject area feature quantities becomes the largest, and weighting corresponding to the sum of the subject area feature quantities is performed for the other band saliency maps. 
     Further, if a sum of feature quantities (information quantities) in a rectangular area corresponding to the rectangular frame  221  that indicates the subject area on a predetermined band saliency map Cm (1=m or 1&lt;m&lt;M or m=M) is taken as a sum cm of subject area feature quantities, the group of weighting factors WC shown in the lower section of  FIG. 13  is calculated. 
     The respective factors in the group of weighting factors WC shown in  FIG. 13  correspond to the respective weighting factors w 1  to wM shown in  FIG. 8 . 
     More specifically, the respective factors in the group of weighting factors WC shown in  FIG. 13  indicate the weighting factors w 1  to wM for the synthesized saliency maps C 1  to CM for each feature quantity shown in  FIG. 8 . As shown in  FIG. 13 , the weighting factors w 1  to wM are factors that take a value from 0 to 1 such that their denominators are maximum values among sums c 1  to cM of subject area feature quantities for the respective synthesized saliency maps C 1  to CM, and their numerators are the sums c 1  to cM of the subject area feature quantities for the respective synthesized saliency maps C 1  to CM. 
     In other words, according to the weighting factors w 1  to wM, among the synthesized saliency maps C 1  to CM for each feature quantity, weighting is performed such that the maximum value becomes 1 for the synthesized saliency map of the feature quantity in which the sum of the subject area feature quantities becomes the largest, and weighting corresponding to the sum of the subject area feature quantities is performed for the other synthesized saliency maps. 
     The weighting factor calculation unit  74  supplies the calculated group of weighting factors WR to the band saliency map synthesis unit  113  of the subject map generation unit  71 . At the same time, the weighting factor calculation unit  74  supplies the group of weighting factors WC to the synthesized saliency map synthesis unit  114  of the subject map generation unit  71 . In the flowchart shown in  FIG. 6 , after performing step S 14 , the subject tracking processing for the next frame is performed, and this processing is repeatedly performed for each frame. 
     With the above-described processing, in the saliency map for each feature quantity relating to a predetermined frame of an input image, in accordance with a relative magnitude of the feature quantity of the area corresponding to the subject area selected in that frame, the weighting factor with respect to the saliency map for each feature quantity for the next frame is decided. Therefore, even in a case where feature quantities vary between frames, a subject map is generated such that the largest weighting is applied to the saliency map of a feature quantity that most appropriately represents the subject among a plurality of feature quantities. Therefore, even in an environment in which the state of the subject varies, it is possible to track the subject more stably. 
     Further, since the subject area is decided such that it includes the whole subject, even in an environment in which the state of a part of the subject area varies, it is possible to track the subject more stably. 
     In a known subject tracking technique, particularly in a case where one of the coordinates in the subject area (or a part of the area including the coordinate) is identified, the whole subject cannot be tracked, and detection frames for auto focus (AF), auto exposure (AE) and auto color control (ACC) cannot be set properly. In a case where a same feature quantity area, which is within the subject area and has the same feature quantity, is identified, accuracy to set a detection frame can be increased compared to the above-described case. However, in many cases, the same feature quantity area is only a small part of the subject area, and sufficient detection accuracy therefore cannot be obtained. 
     On the other hand, according to the above-described subject tracking processing, the subject area including the whole subject can be identified. Therefore, it is possible to increase detection accuracy, and it is also possible to apply a tracking result to a variety of applications. 
     Further, a subject tracking technique is also known that detects and tracks a person by registering a person&#39;s whole image in a dictionary through learning, for example. However, it is not possible to track a subject other than the person or persons registered in the dictionary. Moreover, the amount of information (images) registered in the dictionary becomes a significant amount, which results in a large apparatus size. 
     On the other hand, with the above-described subject tracking processing, it is possible to detect and track any given subject, and further, there is no need to register a significant amount of information in a dictionary or the like. Therefore, it is possible to achieve a compact apparatus size. 
     In the above description, a luminance component, a color component and an edge direction are used as a feature quantity. However, the present invention is not limited to these examples and, for example, motion information may be added. Further, it is preferable, for example, to use feature quantities having a complementary relationship, such as a luminance component and a color component, and such feature quantities may be appropriately selected. 
     In addition, in the above description, M×(N+1) types of weighting factor are calculated corresponding to M×(N+1) types of saliency map. However, by appropriately calculating only weighting factors that correspond to some of the saliency maps, it is possible to reduce a calculation amount in the image processing apparatus  11 . For example, only weighting factors w 1  to wM corresponding to the M types of saliency map of the synthesized saliency maps C 1  to CM may be calculated. 
     Further, in the above description, the area information calculation unit  134  calculates the size of the rectangular frame and the coordinates of the center position of the rectangular frame, as area information of the rectangular area. However, the area information calculation unit  134  may calculate an integral value or a peak value (a maximum value) of pixel values within the rectangular area. In this case, in the subject area selection processing (refer to  FIG. 11 ), a rectangular area having an integral value or a peak value of pixel values within an area that is closest to an integral value or a peak value of pixel values within the subject area one frame before is taken as a subject area. 
     If the image processing apparatus  11  is a digital still camera that captures still images, the user captures a still image by performing a shutter operation, using a shutter triggered by a shutter triggering mechanism, at a desired timing while confirming video (finder images presented in a view finder) displayed on the display unit  34 . 
     As an example of an application to which a tracking result of the above-described subject tracking processing is applied, it is possible to cause the image processing apparatus  11  formed as described above to perform automatic shutter processing, instead of a shutter operation by the user. The automatic shutter processing can capture a still image in response to a change in a state of a tracked subject. 
     (Example of Functional Configuration of Control Unit) 
     Here, a functional configuration of the control unit  35  that performs the automatic shutter processing will be explained with reference to  FIG. 14 . The automatic shutter processing captures a still image in response to a change in the state of the subject tracked by the above-described subject tracking processing. 
     The control unit  35  shown in  FIG. 14  is provided with a coordinate information acquisition unit  331 , an area shape determination unit  332  and an imaging control unit  333 . 
     The coordinate information acquisition unit  331  acquires coordinate information of the subject area that is supplied for each input image frame from the subject tracking unit  55  (refer to  FIG. 1 ), and supplies the coordinate information to the area shape determination unit  332 . 
     The area shape determination unit  332  determines a change in the shape of the subject area between input image frames, based on the coordinate information of the subject area supplied from the coordinate information acquisition unit  331 . More specifically, the area shape determination unit  332  determines a change, between the frames, of the aspect ratio of the subject area, which is a rectangular area expressed by coordinate information of the subject area, and supplies information in accordance with a determination result to the imaging control unit  333 . 
     The imaging control unit  333  controls the imager  32 , the digital signal processing unit  33  and the lens drive unit  36  based on the information supplied from the area shape determination unit  332 , and thereby controls drive of the imaging lens, aperture adjustment, signal processing on image data, recording on a recording medium (not shown in the drawings) and the like. In summary, the imaging control unit  333  controls image capture performed by the image processing apparatus  11 . 
     (Automatic Shutter Processing) 
     Next, the automatic shutter processing performed by the image processing apparatus  11  will be explained with reference to a flowchart shown in  FIG. 15 . 
     At step S 311 , the subject tracking unit  55  performs the subject tracking processing explained with reference to the flowchart shown in  FIG. 6 , and supplies coordinate information of the subject area to the control unit  35 . 
     At step S 312 , the coordinate information acquisition unit  331  acquires the coordinate information of the subject area from the subject tracking unit  55 , and supplies the coordinate information to the area shape determination unit  332 . 
     At step S 313 , the area shape determination unit  332  monitors the aspect ratio of the subject area in an input image, for each frame, based on the coordinate information of the subject area supplied from the coordinate information acquisition unit  331 , and determines whether or not the aspect ratio of the subject area has changed between the frames significantly with respect to a predetermined threshold value. 
     When it is determined at step S 313  that the aspect ratio of the subject area has not significantly changed with respect to the predetermined threshold value, the processing returns to step S 311  and processing from step S 311  to step S 313  is repeated. 
     On the other hand, when it is determined at step S 313  that the aspect ratio of the subject area has significantly changed with respect to the predetermined threshold value, the area shape determination unit  332  supplies to the imaging control unit  333  information indicating that the aspect ratio of the subject area has significantly changed with respect to the predetermined threshold value. 
     For example, as shown in the left section of  FIG. 16 , it is assumed that a running child, who is a subject, is in an input image of an (n−1)-th frame. Here, if the height of a subject frame H (n−1), which indicates the subject area in the input image of the (n−1)-th frame, is denoted by Hh (n−1) and the width of the subject frame H (n−1) is denoted by Hw (n−1), an aspect ratio P(n−1) of the subject area is expressed as Hh (n−1)/Hw (n−1). 
     Then, as shown in the right section of  FIG. 16 , if the child, who is the subject, has just fallen down in an input image of an n-th frame, an aspect ratio P(n)=Hh (n)/Hw (n) of the subject area in the input image of the n-th frame changes compared to the aspect ratio P(n−1) of the subject area in the input image of the (n−1)-th frame. 
     At this time, if it is determined by the area shape determination unit  332  that a difference |P(n)−P(n−1)| between the aspect ratio P(n−1) of the subject area in the input image of the (n−1)-th frame and the aspect ratio P(n) of the subject area in the input image of the n-th frame is larger than a predetermined threshold value, information indicating that the aspect ratio of the subject area has significantly changed with respect to the predetermined threshold value is supplied to the imaging control unit  333 . 
     Returning to the flowchart shown in  FIG. 15 , if the information indicating that the aspect ratio of the subject area has significantly changed with respect to the predetermined threshold value is supplied from the area shape determination unit  332  at step S 314 , the imaging control unit  333  supplies information indicating an image capture command to the imager  32 , the digital signal processing unit  33  and the lens drive unit  36 . In response to this, the digital signal processing unit  33  performs predetermined signal processing on image data corresponding to the input image of the n-th frame shown in  FIG. 16 . The resultant image data is recorded on the recording medium (not shown in the drawings). 
     With the above-described processing, when the aspect ratio of the subject area including the subject has significantly changed, a still image is captured. Thus, image capture can be performed without missing a decisive moment, such as the moment when the child has just fallen down as explained with reference to  FIG. 16 . Further, in the subject tracking processing, if a bird is selected as a subject, it is possible to capture an image at a moment when the bird flaps its wings, for example, due to a change in the aspect ratio of the subject frame (the subject area) that surrounds the bird. In this manner, even when the subject is other than a person and does not have a facial expression, it is possible to more reliably obtain a best shot image. 
     Note that, although in the above description, the aspect ratio of the subject area is expressed by (height of the subject area)/(width of the subject area), it may be expressed as (width of the subject area)/(height of the subject area). 
     Further, although in the above description, a change in the aspect ratio of the subject area between frames is determined, simply, a change in the height or width of the subject area between frames may be determined. 
     Although in the above description, a still image is captured when the state of the subject changes, a still image may be captured when the change in the state of the subject stops. 
     (Another Example of Functional Configuration of Control Unit) 
     Given this, an example of a functional configuration of the control unit  35  provided in the image processing apparatus  11  that captures a still image when the change in the state of the subject stops will be explained with reference to  FIG. 17 . 
     Note that, in the control unit  35  shown in  FIG. 17 , structural elements having the same functions as those of the structural elements provided in the control unit  35  shown in  FIG. 14  are denoted by the same names and the same reference numerals and an explanation thereof is omitted as appropriate. 
     More specifically, the control unit  35  shown in  FIG. 17  is different from the control unit  35  shown in  FIG. 14  in that an area shape determination unit  431  is provided in place of the area shape determination unit  332 . 
     Based on the coordinate information of the subject area supplied from the coordinate information acquisition unit  331 , the area shape determination unit  431  determines a change, across a predetermined number of frames, in the aspect ratio of the subject area that is a rectangular area indicated by the coordinate information of the subject area. Then, the area shape determination unit  431  supplies information in accordance with a determination result to the imaging control unit  333 . 
     (Automatic Shutter Processing) 
     Next, automatic shutter processing performed by the image processing apparatus  11  provided with the control unit  35  shown in  FIG. 17  will be explained with reference to a flowchart shown in  FIG. 18 . 
     Note that processing at step S 411 , step S 412  and step S 414  of the flowchart shown in  FIG. 18  is basically the same as the processing at step S 311 , step S 312  and step S 314  of the flowchart shown in  FIG. 15 , and an explanation thereof is therefore omitted. 
     Specifically, at step S 413 , based on the coordinate information of the subject area supplied from the coordinate information acquisition unit  331 , the area shape determination unit  431  monitors the aspect ratio of the subject area in the input image for each frame, and determines whether or not the aspect ratio of the subject area has changed for a predetermined number of frames. 
     When it is determined at step S 413  that the aspect ratio of the subject area has changed for the predetermined number of frames, the processing returns to step S 411  and the processing from step S 411  to step S 413  is repeated. 
     On the other hand, when it is determined at step S 413  that the aspect ratio of the subject area has not changed for the predetermined number of frames, the area shape determination unit  431  supplies, to the imaging control unit  333 , information indicating that the aspect ratio of the subject area has not changed for the predetermined number of frames. 
     For example, when a variation width of the aspect ratio P(n−q),. . . , p (n) of the subject area is almost not detected for q frames from an (n−q)-th frame to an n-th frame, namely, when the change in the state of the subject has stopped, information indicating that the aspect ratio of the subject area has not changed for the predetermined number of frames is supplied to the imaging control unit  333 . In response to this, a command to capture the input image of the n-th frame is issued from the imaging control unit  333 . 
     With the above-described processing, when the aspect ratio of the subject area including the subject has not changed for the predetermined number of frames, a still image is captured. Thus, it is possible to perform image capture without missing a few seconds when the child, who has been moving around and repeatedly standing up and crouching down, stops moving, for example. Further, in the subject tracking processing, when a bird is selected as a subject, it is possible to perform image capture for a few seconds when the bird does not flap its wings in the air. In this manner, even when the subject is other than a person and does not have a facial expression, it is possible to more reliably obtain a best shot image. 
     In the above description, a still image is captured in response to a change in the state of the subject. However, in this case, the still image is captured regardless of the position of the subject on the input image. Therefore, there are cases in which an image in which the subject is arranged near the end of the image is obtained. There is a high possibility that such an image is not considered to have a good composition. 
     (Yet Another Example of Functional Configuration of Control Unit) 
     Given this, an example of a functional configuration of the control unit  35  provided in the image processing apparatus  11  that captures a still image in response to a position of a subject and a change in the state of the subject will be explained with reference to  FIG. 19 . 
     Note that, in the control unit  35  shown in  FIG. 19 , structural elements having the same functions as those of the structural elements of the control unit  35  shown in FIG.  14  are denoted by the same names and the same reference numerals and an explanation thereof is omitted as appropriate. 
     More specifically, the control unit  35  shown in  FIG. 19  is different from the control unit  35  shown in  FIG. 14  in that a position detection unit  531  is additionally provided. 
     The position detection unit  531  detects the position of the subject in a predetermined frame of the input image, based on the coordinate information of the subject area supplied from the coordinate information acquisition unit  331 . In accordance with the detected position, the position detection unit  531  supplies to the area shape determination unit  332  the coordinate information of the subject area that has been supplied from the coordinate information acquisition unit  331 . 
     (Automatic Shutter Processing) 
     Next, automatic shutter processing performed by the image processing apparatus  11  provided with the control unit  35  shown in  FIG. 19  will be explained with reference to a flowchart shown in  FIG. 20 . 
     Note that, processing at step S 511 , step S 512 , step S 514  and step S 515  of the flowchart shown in  FIG. 20  is basically the same as the processing at step S 311  to step S 314  of the flowchart shown in  FIG. 15 , and an explanation thereof is therefore omitted. 
     Specifically, at step S 513 , based on the coordinate information of the subject area supplied from the coordinate information acquisition unit  331 , the position detection unit  531  monitors the position of the subject area in the input image for each frame, and determines whether or not the position of the subject area is within a predetermined area in the input image. The position of the subject area detected by the position detection unit  531  may be coordinates of all four vertices of the subject area, which is a rectangular area, or may be coordinates of the center position of the subject area. Further, it is assumed that the predetermined area is set in the input image, in the vicinity of the center of the input image. 
     When it is determined at step S 513  that the position of the subject area is not within the predetermined area, the processing returns to step S 511 , and the processing from step S 511  to step S 513  is repeated. 
     On the other hand, when it is determined at step S 513  that the position of the subject area is within the predetermined area, the position detection unit  531  supplies, to the area shape determination unit  332 , the coordinate information of the subject area supplied from the coordinate information acquisition unit  331 . 
     As a result, in a case where the subject area is within an area A shown by a dotted line as shown in  FIG. 21 , if it is determined by the area shape determination unit  332  that a difference |P(n)−P(n−1)| between the aspect ratio P(n−1) of the subject area in the input image of the (n−1)-th frame and the aspect ratio P(n) of the subject area in the input image of the n-th frame is larger than a predetermined threshold value, information indicating that the aspect ratio of the subject area has significantly changed with respect to the predetermined threshold value is supplied to the imaging control unit  333 . In response to this, a command to capture the input image of the n-th frame is issued from the imaging control unit  333 . 
     With the above-described processing, when the aspect ratio of the subject area including the subject has changed significantly in the predetermined area on the input image, a still image is captured. Thus, as shown in  FIG. 21 , it is possible to capture an image with a better composition without missing a decisive moment, such as the moment when a child has just fallen down, for example. Further, if a bird is selected as a subject in the subject tracking processing, it is possible to capture an image with a better composition at a moment when the bird flaps its wings, for example, due to a change in the aspect ratio of the subject frame (the subject area) that surrounds the bird. In this manner, even when the subject is other than a person and does not have a facial expression, it is possible to more reliably obtain a best shot image with a better composition. 
     Note that, in the above description, a still image is captured when the state of the subject changes in the predetermined area on the input image. However, if the control unit  35  shown in  FIG. 19  is provided with the area shape determination unit  431  shown in  FIG. 17  instead of the area shape determination unit  332 , it is also possible to capture a still image when the change in the state of the subject stops in the predetermined area on the input image. 
     Further, although in the above description, it is assumed that the predetermined area is set in the vicinity of the center of the input image, it can also be set by the user at a desired position on the input image. Thus, it is possible to capture an image in a user&#39;s desired composition. 
     In the above description, a still image is captured in accordance with a change in the state of the subject, which is not limited to being a person. When the subject is a person, the face of the person may be detected and a still image of the person may be captured in accordance with a relationship between the whole subject (person) and the face. 
     (Another Example of Configuration of Image Processing Apparatus) 
       FIG. 22  shows an example of a configuration of an image processing apparatus  611  that detects the face of a person as a subject, and captures a still image in accordance with a relationship between the whole subject (person) and the face. 
     Note that, in the image processing apparatus  611  shown in  FIG. 22 , structural elements having the same functions as those of the structural elements provided in the image processing apparatus  11  shown in  FIG. 1  are denoted by the same names and the same reference numerals and an explanation thereof is omitted as appropriate. Specifically, the image processing apparatus  611  shown in  FIG. 22  is different from the image processing apparatus  11  shown in  FIG. 1  in that a face detection unit  621  is additionally provided in the digital signal processing unit  33 , and a control unit  622  is provided instead of the control unit  35 . 
     Based on image data formed of a luminance signal and a color signal generated by the YC generation unit  53 , the face detection unit  621  detects a face, in an input image displayed by the image data, from the subject area of the person as a subject detected by the subject tracking unit  55 . Then, the face detection unit  621  supplies coordinate information indicating an area of the face (hereinafter referred to as a face area) to the control unit  622 . 
     Based on the subject area supplied from the subject tracking unit  55  and the coordinate information of the face area supplied from the face detection unit  621 , the control unit  622  performs automatic shutter processing that captures still images. 
     (Example of Functional Configuration of Control Unit) 
     Here, an example of a functional configuration of the control unit  622  will be explained with reference to  FIG. 23 . 
     Note that, an imaging control unit  633  provided in the control unit  622  shown in  FIG. 23  has basically the same function as that of the imaging control unit  333  provided in the control unit  35  shown in  FIG. 14 , and an explanation thereof is therefore omitted. 
     A coordinate information acquisition unit  631  acquires the coordinate information of the subject area that is supplied from the subject tracking unit  55  for each frame of the input image, and also acquires the coordinate information of the face area that is supplied from the face detection unit  621  for each frame of the input image, and supplies the acquired coordinate information to an area shape determination unit  632 . 
     Based on the coordinate information of the subject area and the face area supplied from the coordinate information acquisition unit  631 , the area shape determination unit  632  determines a change in the ratio of the subject area and the face area between frames, and supplies information in accordance with a determination result to the imaging control unit  633 . 
     (Automatic Shutter Processing) 
     Next, the automatic shutter processing performed by the image processing apparatus  611  shown in  FIG. 22  that is provided with the control unit  622  shown in  FIG. 23  will be explained with reference to a flowchart shown in  FIG. 24 . 
     Note that, processing at step S 611  and step S 615  of the flowchart shown in  FIG. 24  is basically the same as the processing at step S 311  and step S 314  of the flowchart shown in  FIG. 15 , and an explanation thereof is therefore omitted. 
     Specifically, at step S 612 , the face detection unit  621  detects a face in the input image, from the subject area of the person that is the subject detected in the subject tracking processing performed by the subject tracking unit  55 . Then, the face detection unit  621  supplies coordinate information indicating the face area to the control unit  622 . 
     At step S 613 , the coordinate information acquisition unit  631  acquires the coordinate information of the subject area and the coordinate information of the face area respectively supplied from the subject tracking unit  55  and the face detection unit  621 , and supplies the acquired coordinate information to the area shape determination unit  632 . 
     At step S 614 , based on the coordinate information of the subject area and the face area supplied from the coordinate information acquisition unit  631 , the area shape determination unit  632  monitors the ratio of the subject area and the face area in the input image for each frame, and determines whether or not the ratio of the subject area and the face area has significantly changed with respect to a predetermined threshold value between the frames. 
     More specifically, based on the coordinate information of the subject area and the face area supplied from the coordinate information acquisition unit  631 , the area shape determination unit  632  determines whether or not a ratio Fh/Hw (where Fh is the height of a face frame F indicating the face area, and Hw is the width of a subject frame H indicating the subject area) has changed significantly between frames with respect to the predetermined threshold value. 
     When it is determined at step S 614  that the ratio of the subject area and the face area has not significantly changed with respect to the predetermined threshold value, the processing returns to step S 611 , and the processing from step S 611  to step S 614  is repeated. 
     On the other hand, when it is determined at step S 614  that the ratio of the subject area and the face area has significantly changed with respect to the predetermined threshold value, the area shape determination unit  632  supplies to the imaging control unit  633  information indicating that the ratio of the subject area and the face area has significantly changed with respect to the predetermined threshold value. 
     For example, as shown on the left side of  FIG. 25 , when a running child, who is a subject, is in the input image of the (n−1)-th frame, a ratio Q(n−1) of the subject area and the face area is expressed as Fh (n−1)/Hw (n−1), where Fh (n−1) is the height of a face frame F (n−1) indicating the face area, and Hw (n−1) is the width of a subject frame H (n−1) indicating the subject area. 
     Then, as shown on the right side of  FIG. 25 , if the child, who is the subject, has just fallen down in the input image of the n-th frame, Q(n)=Fh (n)/Hw (n), which is the ratio of the subject area and the face area in the input image of the n-th frame, has changed compared to the ratio Q(n−1) of the subject area and the face area in the input image of the (n−1)-th frame. 
     At this time, if it is determined by the area shape determination unit  632  that a difference |Q(n)−Q(n−1)| between the ratio Q(n−1) of the subject area and the face area in the input image of the (n−1)-th frame and the ratio Q(n) of the subject area and the face area in the input image of the n-th frame is larger than a predetermined threshold value, information indicating that the ratio of the subject area and the face area has significantly changed with respect to the predetermined threshold value is supplied to the imaging control unit  633 . In response to this, a command to capture the input image of the n-th frame is issued from the imaging control unit  633 . 
     With the above-described processing, a still image is captured when the ratio of the subject area and the face area has changed significantly. As a result, it is possible to perform image capture without missing a decisive moment, such as the moment when the child has just fallen down as shown in  FIG. 25 , and it is therefore possible to more reliably obtain a best shot image. 
     Note that, if the control unit  622  shown in  FIG. 23  further includes the position detection unit  531  shown in  FIG. 19  at a later stage of the coordinate information acquisition unit  631 , it is also possible to capture a still image when the ratio of the subject area and the face area has changed significantly in a predetermined area of the input image. 
     Further, in the above description, a still image is captured when the ratio of the subject area of the subject, which is a person, and the face area of the face, which is a part of the person, has changed. However, if a subject and a part of the subject can be respectively detected, it is possible to capture an image of a subject other than a person, in response to a change in the ratio of the respective areas 
     Although in the above description, a still image is captured when the ratio of the subject area and the face area has changed, a still image may be captured when the ratio of the subject area and the face area reaches a value determined in advance. 
     (Another Example of Functional Configuration of Control Unit) 
     Given this, an example of a functional configuration of the control unit  622  provided in the image processing apparatus  611  that captures a still image when the ratio of the subject area and the face area reaches a value determined in advance will be explained with reference to  FIG. 26 . 
     Note that, in the control unit  622  shown in  FIG. 26 , structural elements having the same functions as those of the structural elements provided in the control unit  622  shown in  FIG. 23  are denoted by the same names and the same reference numerals and an explanation thereof is omitted as appropriate. 
     More specifically, the control unit  622  shown in  FIG. 26  is different from the control unit  622  shown in  FIG. 23  in that an area ratio comparison unit  731  is provided instead of the area shape determination unit  632 . 
     Based on the coordinate information of the subject area and the face area supplied from the coordinate information acquisition unit  631 , the area ratio comparison unit  731  compares the ratio of the subject area and the face area in a predetermined frame of the input image with a target value determined in advance, and supplies information in accordance with a comparison result to the imaging control unit  633 . Note that the target value can be set by the user as desired. 
     (Automatic Shutter Processing) 
     Next, automatic shutter processing performed by the image processing apparatus  611  shown in  FIG. 22  provided with the control unit  622  shown in  FIG. 26  will be explained with reference to a flowchart shown in  FIG. 27 . 
     Note that, processing at step S 711  to step S 713  and step S 715  of the flowchart shown in  FIG. 27  is basically the same as the processing at step S 611  to step S 613  and step S 615  of the flowchart shown in  FIG. 24 , and an explanation thereof is therefore omitted. 
     Specifically, at step S 714 , based on the coordinate information of the subject area and the face area supplied from the coordinate information acquisition unit  631 , the area ratio comparison unit  731  compares the ratio of the subject area and the face area in a predetermined frame of the input image with the target value determined in advance. 
     More specifically, based on the coordinate information of the subject area and the face area, the area ratio comparison unit  731  determines whether or not a difference between the target value and the ratio of the subject area and the face area is smaller than a predetermined threshold value. 
     When it is determined at step S 714  that the difference between the target value and the ratio of the subject area and the face area is not smaller than the predetermined threshold value, the processing returns to step S 711  and the processing from step S 711  to step S 714  is repeated. 
     On the other hand, when it is determined at step S 714  that the difference between the target value and the ratio of the subject area and the face area is smaller than the predetermined threshold value, namely, when the ratio of the subject area and the face area is the same as the target value or substantially the same as the target value, the area ratio comparison unit  731  supplies, to the imaging control unit  633 , information indicating that the difference between the target value and the ratio of the subject area and the face area is smaller than the predetermined threshold value. 
     For example, as shown on the left side of  FIG. 28 , it is assumed that a child as a subject is running from further back and coming closer to the image processing apparatus  611  in the input image of a p-th frame. Here, a ratio S (p) of the subject area and the face area is expressed as Hh (p)/Fh (p), where Hh (p) is the height of a subject frame H (p) indicating the subject area in the input image of the p-th frame, and Fh (p) is the height of a face frame F (p) indicating the face area. 
     Then, as shown on the right side of  FIG. 28 , when the child as the subject moves in proximity to the image processing apparatus  611  and it is determined, in the input image of an N-th frame, that the difference between the target value and a ratio S (N)=Hh (N)/Fh (N) of the subject area and the face area is smaller than a predetermined threshold value, information indicating that the difference between the target value and the ratio of the subject area and the face area is smaller than the predetermined threshold value is supplied to the imaging control unit  633 . In response to this, a command to capture the input image of the N-th frame is issued from the imaging control unit  633 . 
     With the above-described processing, a still image is captured when the difference between the target value and the ratio of the subject area and the face area is smaller than the predetermined threshold value. As a result, it is possible to capture the moment when the child comes closer and the person&#39;s size (a so-called shot) in the imaging range becomes a best shot to capture an image of the upper half of the body, as shown in  FIG. 28 . Thus, it is possible to more reliably obtain a best shot image. 
     Further, by adjusting the target value, it is possible to capture a still image at a user&#39;s desired shot, such as a full shot that captures the whole subject, a close-up shot that captures the face, and the like. 
     In the above description, the processing performed when the image processing apparatus is formed as a digital still camera that captures still images is explained. When the image processing apparatus is formed as a digital video camera that captures video, it is possible to cause the image processing apparatus to perform frame identification processing, as an example of an application to which a tracking result of the subject tracking processing is applied. The frame identification processing identifies a predetermined frame in video in response to a change in a state of a tracked subject. 
     (Yet Another Example of Image Processing Apparatus) 
     Next, an example of a configuration of an image processing apparatus  811  that performs the frame identification processing will be explained with reference to  FIG. 29 . The frame identification processing identifies a predetermined frame in video, in response to a change in a state of the subject tracked by the above-described subject tracking processing. 
     Note that, in the image processing apparatus  811  shown in  FIG. 29 , structural elements having the same functions as those of the structural elements provided in the image processing apparatus  11  shown in  FIG. 1  are denoted by the same names and the same reference numerals and an explanation thereof is omitted as appropriate. 
     Specifically, the image processing apparatus  811  shown in  FIG. 29  is different from the image processing apparatus  11  shown in  FIG. 1  in that a control unit  821  is provided instead of the control unit  35 . 
     The control unit  821  performs the frame identification processing that identifies a predetermined frame in video, based on the coordinate information of the subject area supplied from the subject tracking unit  55 . 
     (Example of Functional Configuration of Control Unit) 
     Here, an example of a functional configuration of the control unit  821  will be explained with reference to  FIG. 30 . 
     Note that, in the control unit  821  shown in  FIG. 30 , a coordinate information acquisition unit  831  and an area shape determination unit  832  have basically the same functions as those of the coordinate information acquisition unit  331  and the area shape determination unit  332  provided in the control unit  35  shown in  FIG. 14 , and an explanation thereof is therefore omitted. 
     Based on information from the area shape determination unit  832 , a frame identification unit  833  controls the digital signal processing unit  33  such that signal processing is performed in the digital signal processing unit  33  and a predetermined frame of the input image to be recorded on the recording medium (not shown in the drawings) is identified. 
     (Frame Identification Processing) 
     Next, the frame identification processing performed by the image processing apparatus  811  shown in  FIG. 29 , which includes the control unit  821  shown in  FIG. 30 , will be explained with reference to a flowchart shown in  FIG. 31 . 
     Note that, processing at step S 811  to step S 813  of the flowchart shown in  FIG. 31  is basically the same as the processing at step S 311  to step S 313  of the flowchart shown in  FIG. 15 , and an explanation thereof is therefore omitted. 
     Specifically, if information indicating that the aspect ratio of the subject area has changed significantly with respect to a predetermined threshold value is supplied from the area shape determination unit  832 , the frame identification unit  833  controls the digital signal processing unit  33  at step S 814  such that a tag to identify a predetermined frame is added to an input image. As a result, video, to which the tag to identify the predetermined frame is added as metadata, is recorded on the recording medium (not shown in the drawings). 
     With the above-described processing, when the aspect ratio of the subject area including a subject has changed significantly, the tag is added to identify the frame in the video. Thus, in a case where the recorded video is edited, for example, it is possible to easily retrieve a decisive moment, such as the moment when a child has just fallen down. 
     Note that, in the above description, a frame is identified in video when the aspect ratio of the subject area has changed significantly. However, if the control unit  821  shown in  FIG. 30  is provided with the area shape determination unit  431  shown in  FIG. 17  in place of the area shape determination unit  832 , it is also possible to identify a frame in video when the change in the state of the subject has stopped in a predetermined area on the input image. 
     Further, if the control unit  821  shown in  FIG. 30  further includes the position detection unit  531  shown in  FIG. 19  at a later stage of the coordinate information acquisition unit  831 , it is also possible to identify a frame in video when the state of the subject has changed in a predetermined area on the input image. 
     Furthermore, if the digital signal processing unit  33  of the image processing apparatus  811  further includes the face detection unit  621  shown in  FIG. 22  and the control unit  821  shown in  FIG. 30  includes the area shape determination unit  632  shown in  FIG. 23  in place of the area shape determination unit  832 , it is also possible to identify a frame in video when the ratio of the subject area and the face area has changed significantly. 
     Moreover, when the ratio of the subject area and the face area has changed significantly, the frame identification unit  833  may issue to the digital signal processing unit  33  a command to start or stop recording of the video on the recording medium (not shown in the drawings). 
     The above-described series of processing may be performed by hardware or may be performed by software. When the series of processing is performed by software, a program that forms the software is installed in a computer incorporated into a dedicated hardware, or the program is installed from a program storage medium to a general personal computer, for example, that can perform various types of functions by installing various types of programs. 
       FIG. 32  is a block diagram showing an example of a hardware configuration of a computer that performs the above-described series of processing in accordance with a program. 
     In the computer, a central processing unit (CPU)  901 , a read only memory (ROM)  902  and a random access memory (RAM)  903  are mutually connected by a bus  904 . 
     Further, an input/output interface  905  is connected to the bus  904 . An input unit  906 , an output unit  907 , a storage unit  908 , a communication unit  909 , and a drive  910  that drives a removable media  911  are connected to the input/output interface  905 . The input unit  906  includes a keyboard, a mouse, a microphone and the like. The output unit  907  includes a display, a speaker and the like. The storage unit  908  includes a hard disk, a nonvolatile memory and the like. The communication unit  909  includes a network interface and the like. The removable media  911  is a magnetic disk, an optical disk, a magneto optical disk, a semiconductor memory or the like. 
     In the computer configured as described above, the above-described series of processing is performed such that the CPU  901  loads a program stored in, for example, the storage unit  908  into the RAM  903  via the input/output interface  905  and the bus  904 , and executes the program. 
     The program executed by the computer (the CPU  901 ) is provided by recording it in, for example, a magnetic disk (including a flexible disk), an optical disk (a compact disc-read only memory (CD-ROM), a digital versatile disc (DVD) or the like), a magneto optical disk, or the removable media  911  that is a package media formed by a semiconductor memory etc. Alternatively, the above program is provided via a wired or wireless transmission medium, such as a local area network, the Internet and digital satellite broadcasting. 
     The program can be installed in the storage unit  908  via the input/output interface  905 , by attaching the removable media  911  to the drive  910 . Further, the program can be received by the communication unit  909  via a wired or wireless transmission medium and can be installed in the storage unit  908 . Furthermore, the program can be installed in advance in the ROM  902  or the storage unit  908 . 
     Note that the program executed by the computer may be a program in which processing is performed in time series in line with the order explained in this specification, or may be a program in which processing is performed at a necessary timing, such as when a call is performed. 
     The embodiment of the present invention is not limited to the embodiment described above, and various modifications may occur insofar as they fall within the spirit and scope of the present invention. 
     REFERENCE SIGNS LIST 
       11  Image processing apparatus 
       34  Display unit 
       35  Control unit 
       55  Subject tracking unit 
       71  Subject map generation unit 
       72  Subject candidate area rectangular forming unit 
       73  Subject area selection unit 
       74  Weighting factor calculation unit 
       111  Saliency map generation unit 
       112  Band saliency map generation unit 
       113  Band saliency map synthesis unit 
       114  Synthesized saliency map synthesis unit 
       131  Binarization processing unit 
       132  Labeling processing unit 
       133  Rectangular area coordinate calculation unit 
       134  Area information calculation unit 
       151  Area information comparison unit 
       152  Subject area decision unit 
       200  Input image 
       201  Subject map 
       221 ,  222  Rectangular area 
       231  Subject frame 
       332  Area shape determination unit 
       333  Imaging control unit 
       431  Area shape determination unit 
       531  Position detection unit 
       632  Area shape determination unit 
       633  Imaging control unit 
       731  Area ratio comparison unit 
       832  Area shape determination unit 
       833  Frame identification unit

Technology Classification (CPC): 7