Patent Publication Number: US-11030749-B2

Title: Image-processing apparatus, image-processing method, and storage medium storing image-processing program

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This is a continuation of International Application PCT/JP2016/082098, with an international filing date of Oct. 28, 2016, which is hereby incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     The present invention relates to an image-processing apparatus, an image-processing method, and an image-processing program. 
     BACKGROUND ART 
     In the related art, as a form of information in which the degree of visual prominence, that is, saliency, is visualized for the entirety of an image, there is a known saliency map. 
     Patent Literature 1 discloses an image-processing apparatus that calculates a score for each of multiple types of feature quantity by using heterogeneity, the sense of incompatibility, or the like, which is an indicator of saliency, and that predicts a region of a principal imaging subject on the basis of the calculated scores. 
     CITATION LIST 
     Patent Literature 
     
         
         {PTL 1} Publication of Japanese Patent No. 5609425 
       
    
     SUMMARY OF INVENTION 
     A first aspect of the present invention is an image-processing apparatus including: an image processor including circuitry or a hardware processor that operates under control of a stored program, the image processor being configured to execute processes including: a saliency-map calculating process that calculates saliency maps on a basis of at least one type of feature quantity obtained from an input image; a salient-region-identifying process that identifies a salient region by using the saliency maps; a salient-region-score-calculating process that calculates a score of the salient region by comparing a distribution of values of the saliency map in the salient region and a distribution of values of the saliency map in a region other than the salient region; and a saliency-evaluating process that evaluates the saliency of the salient region on a basis of the score. 
     A second aspect of the present invention is an image processor including circuitry or a hardware processor that operates under control of a stored program, the image processor being configured to execute processes including: a saliency-map calculating process that calculates saliency maps on a basis of at least one type of feature quantity obtained from an input image; a salient-region-identifying process that identifies a salient region by using the saliency maps; a salient-region-score-calculating process that calculates a score by using a number of local maximum points in the saliency map in the region other than the salient region; and a saliency-evaluating process that evaluates the saliency of the salient region on a basis of the score. 
     A third aspect of the present invention is an image-processing method including: calculating saliency maps on a basis of at least one type of feature quantity obtained from an input image; identifying a salient region by using the saliency maps; calculating a score of the salient region by comparing a distribution of values of the saliency map in the salient region and a distribution of values of the saliency map in a region other than the salient region; and evaluating the saliency of the salient region on a basis of the score. 
     A fourth aspect of the present invention is a non-transitory computer-readable storage medium storing an image-processing program that causes a computer to execute: calculating saliency maps on a basis of at least one type of feature quantity obtained from an input image; identifying a salient region by using the saliency maps; calculating a score of the salient region by comparing a distribution of values of the saliency map in the salient region and a distribution of values of the saliency map in a region other than the salient region; and evaluating the saliency of the salient region on a basis of the score. 
     A fifth aspect of the present invention is an image-processing method including: calculating saliency maps on a basis of at least one type of feature quantity obtained from an input image; identifying a salient region by using the saliency maps; calculating a score by using a number of local maximum points in the saliency map in a region other than the salient region; and evaluating the saliency of the salient region on a basis of the score. 
     A sixth aspect of the present invention is a non-transitory computer-readable storage medium storing an image-processing program that causes a computer to execute: calculating saliency maps on a basis of at least one type of feature quantity obtained from an input image; identifying a salient region by using the saliency maps; calculating a score by using a number of local maximum points in the saliency map in a region other than the salient region; and evaluating the saliency of the salient region on a basis of the score. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram showing, in outline, image-processing apparatuses according to first to third embodiments of the present invention. 
         FIG. 2  is a diagram showing the internal configuration of the image-processing apparatus according to the first embodiment of the present invention. 
         FIG. 3  is a schematic diagram for explaining a color image, which is an example of an input image. 
         FIG. 4  is a diagram showing the respective internal configurations of a luminance-saliency-map-calculating portion, a color-saliency-map-calculating portion, and a texture-saliency-map-calculating portion provided in the image-processing apparatus shown in  FIG. 2 . 
         FIG. 5A  is a map diagram showing a luminance saliency generated on the basis of the luminance of the color image explained in  FIG. 3 . 
         FIG. 5B  is a diagram showing a color saliency map generated on the basis of the color of the color image explained in  FIG. 3 . 
         FIG. 5C  is a diagram showing a texture saliency map generated on the basis of the texture of the color image explained in  FIG. 3 . 
         FIG. 6A  is a diagram for explaining an example scale in the saliency map. 
         FIG. 6B  is a diagram for explaining another example scale in the saliency map. 
         FIG. 7A  is a diagram for explaining a salient region of the luminance saliency map shown in  FIG. 5A . 
         FIG. 7B  is a diagram for explaining a salient region of the color saliency map shown in  FIG. 5B . 
         FIG. 7C  is a diagram for explaining a salient region of the texture saliency map shown in  FIG. 5C . 
         FIG. 8  is a schematic diagram for explaining a score calculation performed in a salient-region-score-calculating portion in the first embodiment of the present invention. 
         FIG. 9A  is a diagram for explaining the score of the salient region of the luminance saliency map shown in  FIG. 7A   
         FIG. 9B  is a diagram for explaining the score of the salient region of the color saliency map shown in  FIG. 7B . 
         FIG. 9C  is a diagram for explaining the score of the salient region of the texture saliency map shown in  FIG. 7C . 
         FIG. 10  is a flowchart of processing executed in the image-processing apparatus according to the first embodiment of the present invention. 
         FIG. 11  is diagram showing the internal configuration of a salient-region-score-calculating portion provided in the second embodiment of the present invention. 
         FIG. 12  is a schematic diagram for explaining a score calculation performed in the salient-region-score-calculating portion in the second embodiment of the present invention. 
         FIG. 13  is a diagram showing the internal configuration of a salient-region-score-calculating portion provided in an image-processing apparatus according to a third embodiment of the present invention. 
         FIG. 14  is a diagram for explaining an example maximum point of a saliency map. 
         FIG. 15  is a diagram showing the relationship between the number of local maximum points of a saliency map and a correction factor. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Embodiments of an image-processing apparatus according to the present invention will be described below with reference to the drawings. Note that the image-processing apparatus according to these embodiments is provided with a processor (not shown) and storage apparatuses (not shown), such as a ROM (Read Only Memory) and a RAM (Random Access Memory), and is configured to execute, by means of the processor, individual types of processing, described later. Note that, in the following embodiments, although the image-processing apparatus is implemented by causing a computer to execute an image-processing program, which is software, there is no limitation thereto, and the image-processing apparatus may be implemented by means of hardware, such as a circuit on a silicon chip. 
     First Embodiment 
     As shown in  FIG. 1 , an image-processing apparatus  1  according to a first embodiment is provided with, as functional blocks: a saliency-map-calculating portion  11 ; a salient-region-identifying portion  12 ; a salient-region-score-calculating portion  13 ; and a saliency-evaluating portion  14 . 
     The saliency-map-calculating portion  11  is connected to the salient-region-identifying portion  12  and the salient-region-score-calculating portion  13 . The salient-region-identifying portion  12  is connected to the salient-region-score-calculating portion  13 . The salient-region-score-calculating portion  13  is connected to the saliency-evaluating portion  14 . 
     As shown in  FIG. 2 , the saliency-map-calculating portion  11  is provided with a luminance-saliency-map-calculating portion  111 , a color-saliency-map-calculating portion  112 , and a texture-saliency-map-calculating portion  113 , and is configured to individually calculate, on the basis of an input image, a luminance saliency map, which is a saliency map based on the luminance, a color saliency map, which is a saliency map based on the color, and a texture saliency map, which is a saliency map based on the texture. 
     As shown in  FIG. 4 , the luminance-saliency-map-calculating portion  111  is: provided with a feature-quantity-calculating portion  1111  that calculates, with respect to the input image, the luminance, which is a feature quantity, and a difference-calculating portion  1112  that divides the image for which the luminance has been calculated into a plurality of blocks and that calculates differences between luminance of the individual divided blocks and luminance of the blocks on the periphery thereof; and configured to calculate the luminance saliency map. 
     The color-saliency-map-calculating portion  112  is: provided with a feature-quantity-calculating portion  1121  that calculates, with respect to the input image, the color, which is a feature quantity, and a difference-calculating portion  1122  that divides the image for which the color has been calculated into a plurality of blocks and that calculates differences between colors of the individual divided blocks and colors of the blocks on the periphery thereof; and configured to calculate the color saliency map. 
     The texture-saliency-map-calculating portion  113  is: provided with a feature-quantity-calculating portion  1131  that calculates, with respect to the input image, the texture, which is a feature quantity, and a difference-calculating portion  1132  that divides the image for which the texture has been calculated into a plurality of blocks and that calculates differences between textures of the individual divided blocks and textures of the blocks in the periphery thereof; and configured to calculate the texture saliency map. 
     More specifically, in the difference-calculating portions  1112 ,  1122 , and  1132 , one of the plurality of blocks is assumed to be a block of interest, and the differences between the feature quantities of this block of interest and the feature quantities of the blocks in periphery thereof are calculated as the saliency. Then, by sliding the block of interest over the entire image, the saliency maps in which the saliency is visualized for the entire image are generated. 
       FIGS. 5A to 5C  respectively show a luminance saliency map, a color saliency map, and a texture saliency map that are calculated from an input image shown in  FIG. 3 . Note that, although  FIG. 3  shows a schematic diagram of the input image depicted in the form of an illustration, this input image actually is a color image acquired by an image-acquisition apparatus such as a camera. In the actual color image, the lower right portion of  FIG. 3  shows a scene in which three flowers that bear a reddish color and have relatively high saturations are blooming. In addition, a portion corresponding to approximately the lower ⅔ of  FIG. 3  shows a scene in which green leaves that have a relatively fine texture are growing abundantly. In addition, a portion corresponding to approximately the upper ⅓ of  FIG. 3  is a defocused background portion bearing a whitish color. 
     Because the luminance saliency map shown in  FIG. 5A  is generated on the basis of the luminance, the saliency is high in the background portion that bears the whitish color and that has a relatively high luminance. Because the color saliency map shown in  FIG. 5B  is generated on the basis of the color, the saliency is particularly high in the portion having the flowers that bear the reddish color. Because the texture saliency map shown in  FIG. 5C  is generated on the basis of the texture, the saliency is high in the portion corresponding to approximately the lower ⅔ of the image in which the texture is relatively fine. 
     Note that the saliency-map-calculating portion  11  may be configured so that a plurality of saliency maps are calculated for one feature quantity by using a plurality of combinations of the sizes of the blocks and the areas of the peripheral blocks, in other words, scales. In  FIGS. 6A and 6B , examples of different scales are shown. In each of  FIGS. 6A and 6B , the portion that is surrounded by the solid line indicates the block of interest, and the portion that is surrounded by the broken line and that is hatched indicates the peripheral block. The combination of these two is referred to as the scale. By changing the scales of the saliency maps, it is possible to change the size of the imaging subject to which the saliency maps respond. Therefore, by using multiple scales, it is possible to detect imaging subjects of various sizes. 
     The salient-region-identifying portion  12  shown in  FIG. 2  is configured to identify a high-saliency region as a salient region with respect to each of the luminance saliency map, the color saliency map, and the texture saliency map calculated by the saliency-map-calculating portion  11 . The salient-region-identifying portion  12  is configured, for example, so as to identify, in each of the luminance saliency map, the color saliency map, and the texture saliency map, a region consisting of blocks having values that are greater than a threshold set in advance as a salient region.  FIGS. 7A to 7C  individually show, with thick solid lines, the salient regions identified from the individual saliency maps shown in  FIGS. 5A  to  5 C. 
     As shown in  FIG. 2 , the salient-region-score-calculating portion  13  is: provided with an intra-salient-region average value/maximum value calculating portion  131 , an extra-salient-region average value/standard deviation calculating portion  132 , and a salient-region-score-generating portion  133 ; and configured to calculate scores for evaluating the degrees of saliency in the image with respect to the individual salient regions identified by the salient-region-identifying portion  12 . 
     The intra-salient-region average value/maximum value calculating portion  131  is connected to the salient-region-identifying portion  12  and the salient-region-score-generating portion  133 . The extra-salient-region average value/standard deviation calculating portion  132  is connected to the saliency-map-calculating portion  11 , the salient-region-identifying portion  12 , and the salient-region-score-generating portion  133 . The salient-region-score-generating portion  133  is connected to the saliency-evaluating portion  14 . 
     As shown in  FIG. 8 , the intra-salient-region average value/maximum value calculating portion  131  is configured to calculate, on the basis of a salient region R identified by the salient-region-identifying portion  12 , an average Avg(I;R) of the saliency values in the salient region R and a maximum value Max(I;R) of the saliency values. 
     The extra-salient-region average value/standard deviation calculating portion  132  is configured to calculate, on the basis of the saliency maps calculated by the saliency-map-calculating portion  11  and the salient regions identified by the salient-region-identifying portion  12 , an average Avg(I;B) of the saliency values and a standard deviation Stdev(I;B) of the saliency values for a region B, which is a region other than the salient region R. 
     The salient-region-score-generating portion  133  is configured to calculate, on the basis of the average Avg(I;R) and the maximum value Max(I;R) calculated by the intra-salient-region average value/maximum value calculating portion  131  and the average Avg(I;B) calculated by the extra-salient-region average value/standard deviation calculating portion  132 , a difference between a weighted sum of the maximum value Max(I;R) and the average Avg(I;R) and the average Avg(I;B), and calculates this as a score S, as indicated in Expression (1).
 
S =(1−α)Avg( I;R )+αMax( I;R )−Avg( I;B )  (1),
 
where α is weight, and the range within which the values thereof can fall is 0≤α≤1.
 
     In Expression (1), the difference between the weighted sum of the average Avg(I;R) and the maximum value Max(I;R) for the saliency maps in the salient region R and the average Avg(I;B) of the saliency maps in the region B, which is a region other than the salient region R, increases with an increase in the saliency as a region and decreases with a decrease in the saliency as a region. Because of this, by calculating the score on the basis of this difference, it is possible to more appropriately identify a high-saliency region contained in an image. 
     The above-described score S may be normalized by using the variability of a distribution of the saliency values in the region B. The variability of the distribution of the saliency-map values in the region B decreases with an increase in the saliency as a region and increases with a decrease in the saliency as a region. Because of this, by performing the normalization by using this variability, it is possible to more appropriately identify a high-saliency region contained in the image. For example, the standard deviation stdev(I;B) calculated by the extra-salient-region average value/standard deviation calculating portion  132  may be used as an indicator of the variability, and the score S may be calculated by using Expression (2) in which Expression (1) is divided by the standard deviation stdev(I;B). 
     
       
         
           
             
               
                 
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     As a result of Expression (2) being used for calculating the score S, for example, because the standard deviation Stdev(I;B) increases in the case in which the region B has several points at which the saliency values are high, the score S decreases. Conversely, because the standard deviation Stdev(I;B) decreases in the case in which the region B has low saliency values overall, the score S increases. Because of this, by calculating the score S by using the standard deviation Stdev(I;B), it is possible to more appropriately identify a high-saliency region contained in the image. 
       FIGS. 9A to 9C  indicate the scores calculated by means of Expression (2) for the individual salient regions in  FIGS. 7A to 7C . With the color saliency map shown in  FIG. 9B , the score is high as compared with the luminance saliency map shown in  FIG. 9A  and the texture saliency map shown in  FIG. 9C , because the average value of the saliency values of the region other than the salient region is low and the standard deviation is low. 
     The saliency-evaluating portion  14  shown in  FIG. 2  is configured to evaluate the saliency of the identified salient regions on the basis of the scores calculated by the salient-region-score-calculating portion  13 . The saliency-evaluating portion  14  is configured, for example, to evaluate a salient region having the highest score to be a salient region having the highest saliency in the image. 
     In the following, an image-processing method of this embodiment, which is executed by the image-processing apparatus  1  having the above-described configuration, will be described with reference to  FIGS. 1 and 10 . 
     First, as shown in  FIG. 1 , an image is input to the saliency-map-calculating portion  11  (step S 1  in  FIG. 10 ). Next, the saliency-map-calculating portion  11  calculates the saliency maps for different types of feature quantity (step S 2  in  FIG. 10 ). Next, the salient-region-identifying portion  12  identifies the salient regions R on the basis of the saliency maps calculated by the saliency-map-calculating portion  11  (step S 3  in  FIG. 10 ). Subsequently, the salient-region-score-calculating portion  13  calculates, on the basis of the salient regions R identified by the salient-region-identifying portion  12  and the saliency maps calculated by the saliency-map-calculating portion  11 , the scores S of the salient region by comparing the distribution of the saliency-map values in the salient regions R and the distribution of the saliency-map values in the region B (step S 4  in  FIG. 10 ). Finally, the saliency-evaluating portion  14  evaluates the saliency on the basis of the scores S calculated by the salient-region-score-calculating portion. 
     As a result, for example, the salient region having the highest score S is evaluated to be a region having the highest saliency. 
     As has been described above, with this embodiment, because the score S is calculated by using not only the maximum value Max(I;R) and the average Avg(I;R) in the salient region R, but also the average Avg(I;B) and standard deviation Stdev(I;B) in the region B, which is the region other than the salient region R, it is possible to appropriately identify a high-saliency region contained in the image. 
     Note that, with this embodiment, although the weighed sum of the maximum value Max(I;R) and the average Avg(I;R) is employed as an indicator of the distribution of the values in the salient region R, and the average Avg(I;B) is employed as an indicator of the distribution of the values in the region B, there is no limitation thereto. For example, other statistics, such as the median and the mode, which serve as indicators of distributions of values in the individual regions may be employed. 
     Second Embodiment 
     An image-processing apparatus  101  according to a second embodiment will be described below with reference to the drawings. 
     As shown in  FIG. 1 , the image-processing apparatus  101  according to the second embodiment is provided with, as functional blocks: the saliency-map-calculating portion  11 ; the salient-region-identifying portion  12 ; a salient-region-score-calculating portion  23 ; and the saliency-evaluating portion  14 . 
     The saliency-map-calculating portion  11  is connected to the salient-region-identifying portion  12  and the salient-region-score-calculating portion  23 . The salient-region-identifying portion  12  is connected to the salient-region-score-calculating portion  23 . The salient-region-score-calculating portion  23  is connected to the saliency-evaluating portion  14 . 
     As has been described above, the configuration of the image-processing apparatus  101  according to this embodiment differs from that of the image-processing apparatus  1  according to the first embodiment in that a salient-region-score-calculating portion  23  is provided instead of the salient-region-score-calculating portion  13 . In the following description, the portions having the same configurations as those in the image-processing apparatus  1  according to the first embodiment will be given the same reference signs, and the descriptions thereof will be omitted. 
     As shown in  FIG. 11 , the salient-region-score-calculating portion  23  is: provided with an intra-salient-region histogram-generating portion  231 , an extra-salient-region histogram-generating portion  232 , and a salient-region-score-generating portion  233 ; and configured to calculate scores for evaluating the degrees of saliency in the image with respect to the individual salient regions identified by the salient-region-identifying portion  12 . 
     The intra-salient-region histogram-generating portion  231  is connected to the saliency-map-calculating portion  11 , the salient-region-identifying portion  12 , and the salient-region-score-generating portion  233 . The extra-salient-region histogram-generating portion  232  is connected to the saliency-map-calculating portion  11 , the salient-region-identifying portion  12 , and the salient-region-score-generating portion  233 . The salient-region-score-generating portion  233  is connected to the saliency-evaluating portion  14 . 
     As shown in  FIG. 12 , the intra-salient-region histogram-generating portion  231  is configured to create a histogram h R  in which the saliency-map values are plotted on the horizontal axis and the frequency is plotted on the vertical axis for the salient region R identified by the salient-region-identifying portion  12 . 
     The extra-salient-region histogram-generating portion  232  is configured to create, on the basis of the saliency maps calculated by the saliency-map-calculating portion  11  and the salient regions identified by the salient-region-identifying portion  12 , a histogram h B  in which the saliency-map values are plotted on the horizontal axis and the frequency is plotted on the vertical axis for the region B, which is the region other than the salient region R. 
     The salient-region-score-generating portion  233  is configured to calculate, on the basis of the histogram h R  created by the intra-salient-region histogram-generating portion  231  and the histogram h B  created by the extra-salient-region histogram-generating portion  232 , the Bhattacharyya distance, for example, as indicated in Expression (3), and calculates this as the score S.
 
S=d(h R ,h B )=−log(Σ i   n √{square root over (a)}h Ri h Bi )  (3)
 
     Note that, in Expression (3), values h Ri  and h Bi  of the individual bins are calculated as the occurrence probability in which the number of times the bins occur are divided by the number of pixels in the respective regions. 
     As a result of Expression (3) being used to calculate the score S, the score S increases in the case in which the saliency-map distributions greatly differ between the region B, which is the region other than the salient region R, and the salient region R. 
     As has been described above, with this embodiment, because the salient region R is calculated by using not only the histogram h R  for the salient region R but also the histogram h B  for the region B, which is the region other than the salient region R, it is possible to appropriately identify a high-saliency region contained in the image. 
     Third Embodiment 
     An image-processing apparatus  201  according to a third embodiment will be described below with reference to the drawings. 
     As shown in  FIG. 1 , the image-processing apparatus  201  according to the third embodiment is provided with, as functional blocks: the saliency-map-calculating portion  11 ; the salient-region-identifying portion  12 ; a salient-region-score-calculating portion  33 ; and the saliency-evaluating portion  14 . 
     The saliency-map-calculating portion  11  is connected to the salient-region-identifying portion  12  and the salient-region-score-calculating portion  33 . The salient-region-identifying portion  12  is connected to the salient-region-score-calculating portion  33 . The salient-region-score-calculating portion  33  is connected to the saliency-evaluating portion  14 . 
     As has been described above, the configuration of the image-processing apparatus  201  according to this embodiment differs from that of the image-processing apparatus  1  according to the first embodiment in that a salient-region-score-calculating portion  33  is provided instead of the salient-region-score-calculating portion  13 . In the following description, the portions having the same configurations as those in the image-processing apparatus  1  according to the first embodiment will be given the same reference signs, and the descriptions thereof will be omitted. 
     As shown in  FIG. 13 , the salient-region-score-calculating portion  33  is: provided with a saliency-map-maximum-point-detecting portion  331  and a salient-region-score-generating portion  332 ; and configured to calculate scores for evaluating the degrees of saliency in the image with respect to the individual salient regions identified by the salient-region-identifying portion  12 . 
     The saliency-map-maximum-point-detecting portion  331  is configured to detect, on the basis of the saliency maps calculated by the saliency-map-calculating portion  11  and the salient regions identified by the salient-region-identifying portion  12 , maximum values of the saliency maps in the region B, which is the region other than the salient region R and to count the number thereof. For example, in the case of the example shown in  FIG. 14 , local maximum points existing in the region B are detected and the number thereof is counted to be four. 
     The salient-region-score-generating portion  332  is configured to calculate the score S by means of the function indicated in Expression (4).
 
S=n −α   (4),
 
where, α is a parameter that takes an arbitrary positive number, and n indicates the number of local maximum points. As is clear from  FIG. 15 , which shows a graph of Expression (4), in the case in which a maximum point exists in the region B, which is the region other than the salient region R, the score decreases as compared with the case in which the number of local maximum points in the region B is zero.
 
     As has been described above, with this embodiment, by utilizing the number of local maximum points in the region B, which is the region other than the salient region R, it is possible to appropriately identify a high-saliency region contained in the image. 
     Note that, in this embodiment, although the function indicated in Expression (4) is used, there is no limitation thereto, and a function in which S monotonically decreases with respect to n may be used. 
     As above, although the embodiments of the present invention have been described in detail with reference to the drawings, specific configurations are not limited to these embodiments, and design alterations within a range that does not depart from the scope of the present invention are also encompassed. 
     For example, the score obtained by the salient-region-score-calculating portion  33  provided in the image-processing apparatus  201  according to the third embodiment may be used as a correction factor, and the score obtained by the salient-region-score-calculating portion  13  provided in the image-processing apparatus  1  according to the first embodiment or the score obtained by the salient-region-score-calculating portion  23  provided in the image-processing apparatus  101  according to the second embodiment may be multiplied by the correction factor to calculate a final score. 
     In addition, in the above-described embodiment, although the saliency maps are calculated by the saliency-map-calculating portion  11  from the three types of feature quantity, that is, the luminance, the color, and the texture, these three types of feature quantity are not necessarily required, and the saliency maps may be calculated from at least one type of feature quantity. 
     In addition, the feature quantities obtained from the input image are not limited to the above-described three feature quantities, and it is possible to utilize various types of feature quantity that could be obtained from the input image. 
     As a result, the above-described embodiments lead to the following aspects. 
     A first aspect of the present invention is an image-processing apparatus including: a saliency-map calculating portion that is configured to calculate saliency maps on the basis of at least one type of feature quantity obtained from an input image; a salient-region-identifying portion that is configured to identify a salient region by using the saliency maps; a salient-region-score-calculating portion that is configured to calculate a score of the salient region by comparing a distribution of values of the saliency map in the salient region and a distribution of values of the saliency map in a region other than the salient region; and a saliency-evaluating portion that is configured to evaluate the saliency of the salient region on the basis of the score. 
     With the image-processing apparatus according to the above-described first aspect, the saliency-map calculating portion calculates the saliency maps based on at least one type of feature quantity, which is obtained from the input image, and the salient-region-identifying portion identifies the salient region by using the saliency maps. Then, the salient-region-score-calculating portion compares the distribution of the values of the saliency map in the salient region and the distribution of the values of the saliency map in the region other than the salient region, and calculates the score of the salient region. On the basis of the calculated score, the saliency-evaluating portion evaluates the saliency of the salient region. 
     In this way, because the score of the salient region is calculated by using not only the distribution of the values of the saliency map in the salient region but also the distribution of the values of the saliency map in the region other than the salient region, it is possible to appropriately identify a high-saliency region contained in the image. 
     In the image-processing apparatus according to the above-described first aspect, the salient-region-score-calculating portion may be configured to calculate the score on the basis of a difference between a weighted sum of an average value and a maximum value of the saliency map in the salient region and an average value of the saliency map in the region other than the salient region. 
     The difference between the weighted sum of the average value and the maximum value of the saliency map in the salient region and the average value of the saliency map in the region other than the salient region decreases with an increase in the saliency as a region. Because of this, by calculating the score on the basis of this difference, it is possible to more appropriately identify a high-saliency region contained in the image. 
     In the image-processing apparatus according to the above-described first aspect, the salient-region-score-calculating portion may be configured to normalize the score by using the variability of the distribution of the values of the saliency map in the region other than the salient region. 
     The variability of the distribution of the values of the saliency map in the region other the salient region decreases with an increase in the saliency as a region, and increases with a decrease in the saliency as a region. Because of this, by using this variability, it is possible to more appropriately identify a high-saliency region contained in the image. 
     In the image-processing apparatus according to the above-described first aspect, the salient-region-score-calculating portion may be configured to use, as the variability of the distribution of the values of the saliency map in the region other than the salient region, a standard deviation of the values of the saliency map. 
     The standard deviation of the distribution of the values of the saliency map in the region other than the salient region decreases with an increase in the saliency as a region and increases with a decrease in the saliency as a region. Because of this, by using this standard deviation, it is possible to more appropriately identify a high-saliency region contained in the image. 
     In the image-processing apparatus according to the above-described first aspect, the salient-region-score-calculating portion may be configured to calculate, at multiple scales, the saliency maps for the respective types of feature quantity. 
     By using the multiple scales, it is possible to change, for different scales, the size of the imaging subject to which the saliency map responds, and therefore, it is possible to detect imaging subjects of various sizes. 
     In the image-processing apparatus according to the above-described first aspect, the salient-region-score-calculating portion may be configured to calculate the score by calculating a difference between a histogram of the values of the saliency map in the salient region and a histogram of the values of the saliency map in the region other than the salient region. 
     The difference between the histogram of the values of the saliency map in the salient region and the histogram of the values of the saliency map in the region other than the salient region increases with an increase in the saliency as a region and decreases with a decrease in the saliency as a region. Because of this, by calculating the score on the basis of this difference, it is possible to more appropriately identify a high-saliency region contained in the image. 
     A second aspect of the present invention is an image-processing apparatus including: a saliency-map calculating portion that is configured to calculate saliency maps on the basis of at least one type of feature quantity obtained from an input image; a salient-region-identifying portion that is configured to identify a salient region by using the saliency maps; a salient-region-score-calculating portion that is configured to calculate a score by using the number of local maximum points in the saliency map in the region other than the salient region; and a saliency-evaluating portion that is configured to evaluate the saliency of the salient region on the basis of the score. 
     With the above-described image-processing apparatus according to the second aspect, the saliency-map calculating portion calculates the saliency maps based on at least one type of feature quantity, which is obtained from the input image, and the salient-region-identifying portion identifies the salient region by using the saliency maps. Then, the salient-region-score-calculating portion calculates the score of the salient region by using the number of local maximum points in the saliency map in the region other than the salient region. On the basis of the calculated score, the saliency-evaluating portion evaluates the saliency of the salient region. 
     In this way, because the score of the salient region is calculated by using the number of local maximum points in the saliency map in the region other than the salient region, it is possible to appropriately identify a high-saliency region contained in the image. 
     A third aspect of the present invention is an image-processing method including: a step of calculating saliency maps on the basis of at least one type of feature quantity obtained from an input image; a step of identifying a salient region by using the saliency maps; a step of calculating a score of the salient region by comparing a distribution of values of the saliency map in the salient region and a distribution of values of the saliency map in a region other than the salient region; and a step of evaluating the saliency of the salient region on the basis of the score. 
     A fourth aspect of the present invention is an image-processing program that causes a computer to execute: processing for calculating saliency maps on the basis of at least one type of feature quantity obtained from an input image; processing for identifying a salient region by using the saliency maps; processing for calculating a score of the salient region by comparing a distribution of values of the saliency map in the salient region and a distribution of values of the saliency map in a region other than the salient region; and processing for evaluating the saliency of the salient region on the basis of the score. 
     A fifth aspect of the present invention is an image-processing method including: a step of calculating saliency maps on the basis of at least one type of feature quantity obtained from an input image; a step of identifying a salient region by using the saliency maps; a step of calculating a score by using the number of local maximum points in the saliency map in a region other than the salient region; and a step of evaluating the saliency of the salient region on the basis of the score. 
     A sixth aspect of the present invention is an image-processing program that causes a computer to execute: processing for calculating saliency maps on the basis of at least one type of feature quantity obtained from an input image; processing for identifying a salient region by using the saliency maps; processing for calculating a score by using the number of local maximum points in the saliency map in a region other than the salient region; and processing for evaluating the saliency of the salient region on the basis of the score. 
     The present invention affords an advantage in that it is possible to appropriately identify a high-saliency region contained in the image. 
     REFERENCE SIGNS LIST 
     
         
           1 ,  101 ,  201  image-processing apparatus 
           11  saliency-map calculating portion 
           12  salient-region-identifying portion 
           13 ,  23 ,  33  salient-region-score-calculating portion 
           14  saliency-evaluating portion