Co-saliency detection via inter and intra saliency propagation

The goal of salient object detection from an image is to extract the regions which capture the attention of the human visual system more than other regions of the image. In this paper a novel method is presented for detecting salient objects from a set of images, known as co-saliency detection. We treat co-saliency detection as a two-stage saliency propagation problem. The first inter-saliency propagation stage utilizes the similarity between a pair of images to discover common properties of the images with the help of a single image saliency map. With the pairwise co-salient foreground cue maps obtained, the second intra-saliency propagation stage refines pairwise saliency detection using a graph-based method combining both foreground and background cues. A new fusion strategy is then used to obtain the co-saliency detection results. Finally an integrated multi-scale scheme is employed to obtain pixel-level co-saliency maps. The proposed method makes use of existing saliency detection models for co-saliency detection and is not overly sensitive to the initial saliency model selected. Extensive experiments on three benchmark databases show the superiority of the proposed co-saliency model against the state-of-the-art methods both subjectively and objectively