Color Feature Reinforcement for Cosaliency Detection Without Single Saliency Residuals
Rui Huang, Wei Feng, Ji-zhou SUN · IEEE Signal Processing Letters · 2017
Cosaliency detects the common salient objects within a group of images. Hence, those objects that are salient only in individual image or small portion of the image group should conceptually be treated as background. However, most state-of-the-art methods cannot do this well because they measure cosaliency as an explicit combination of single-image saliency and interimage similarity, thus inevitably leaving single saliency residuals into the cosaliency maps. In this letter, we show such problem can be solved by color feature reinforcement, based on a simple observation that cosalient objects usually have similar color distributions in an abundant color feature space. Since we model the cosaliency of an image w.r.t. another one as a reinforced product of the foreground dictionary and sparsely coded saliency coefficients of the two images, respectively, within a same rich feature space, we can effectively eliminate the single saliency residual effect in cosaliency detection. Extensive experiments validate the superior performance of the proposed approach on benchmark datasets.