An image processing device comprising: an image acquisition unit configured to acquire an image ; a similarity map generator configured to generate a first similarity map based on a first algorithm, the first similarity map representing a degree of similarity between a region made up of one pixel or a plurality of adjacent pixels and a marginal region (33-1 to 33-Ns) of the image , the marginal region (33-1 to 33-Ns) including an end portion of the image ; and a saliency map generator configured to generate a saliency map based on at least the first similarity map, characterized in that the similarity map generator is further configured to generate a second similarity map based on a second algorithm, the second similarity map representing a degree of similarity between the region and the marginal region (33-1 to 33-Ns) of the image; and the saliency map generator is further configured to integrate the first similarity map and the second similarity map to generate the saliency map; wherein the first algorithm is configured to determine a dense recovery error map as the first similarity map by calculating a plurality of eigenvectors by performing a principal component analysis on a feature vector of the marginal region (33-1 to 33-Ns), performing a first reconfiguration of a feature vector of a target region in the image by a linear combination of the plurality of eigenvectors, and calculating a first reconfiguration error denoting an error made when performing the first reconfiguration, wherein the degree of similarity increases with decreasing first reconfiguration error, wherein the second algorithm is configured to determine a sparse recovery error map as the second similarity map by performing a second reconfiguration of the feature vector of the target region in the image by a linear combination of a minimum number of feature vectors of the marginal region (33-1 to 33-Ns) using sparse coding, and calculating a second reconfiguration error denoting an error made when performing the second reconfiguration, wherein the degree of similarity increases with decreasing second reconfiguration error. an image acquisition unit configured to acquire an image ; a similarity map generator configured to generate a first similarity map based on a first algorithm, the first similarity map representing a degree of similarity between a region made up of one pixel or a plurality of adjacent pixels and a marginal region (33-1 to 33-Ns) of the image , the marginal region (33-1 to 33-Ns) including an end portion of the image ; and a saliency map generator configured to generate a saliency map based on at least the first similarity map, the similarity map generator is further configured to generate a second similarity map based on a second algorithm, the second similarity map representing a degree of similarity between the region and the marginal region (33-1 to 33-Ns) of the image; and the saliency map generator is further configured to integrate the first similarity map and the second similarity map to generate the saliency map; calculating a plurality of eigenvectors by performing a principal component analysis on a feature vector of the marginal region (33-1 to 33-Ns), performing a first reconfiguration of a feature vector of a target region in the image by a linear combination of the plurality of eigenvectors, and calculating a first reconfiguration error denoting an error made when performing the first reconfiguration, wherein the degree of similarity increases with decreasing first reconfiguration error, performing a second reconfiguration of the feature vector of the target region in the image by a linear combination of a minimum number of feature vectors of the marginal region (33-1 to 33-Ns) using sparse coding, and calculating a second reconfiguration error denoting an error made when performing the second reconfiguration, wherein the degree of similarity increases with decreasing second reconfiguration error. <EOS>