Saliency detection: A self-ordinal resemblance approach

In saliency detection, regions attracting visual attention need to be highlighted while effectively suppressing non-salient regions for the semantic scene understanding. However, most previous methods tend to fail in suppressing highly textured backgrounds and also high contrast edges belonging to the non-salient regions. To address this problem, we propose a method for detecting salient regions based on a self-ordinal resemblance measure (SORM). Our saliency map is defined by using the center-surround computations based on the ordinal signatures obtained from local regions centered at each pixel. It can be regarded as an energy map and thus extended to image retargeting. Our approach is fully automatic and nonparametric. To justify robustness of our approach, the proposed method is compared with the state of the art methods on various images.