Low-rank weighted co-saliency detection via efficient manifold ranking

Co-saliency detection, which aims to detect common salient objects in a group of images, has attracted much attention in the field of computer vision. In this paper, we present an effective co-saliency detection approach that first exploits an efficient manifold ranking scheme to extract a set of co-saliency regions, and then renders rank constraint to the feature matrix of the extracted regions to achieve a high-quality co-saliency map. Specifically, for each input image, we first develop a two-stage manifold ranking algorithm to generate multiple coarse co-saliency maps, and then we extract a group of co-salient regions from each image by fusing the co-saliency maps and the superpixels extracted from it. Then, we design an adaptive weight for each co-saliency map based on the sparse error matrix that is obtained by rendering rank constraint on the feature matrix of the salient regions. Finally, we multiply the coarse co-saliency maps with their corresponding weights to get the fine fusion results, which are further optimized by Graph cuts. Extensive evaluations on the iCoseg dataset demonstrate favorable performance of the proposed approach over some state-of-art methods in terms of both qualitative and quantitative results.