Co-saliency detection via similarity-based saliency propagation

In this paper, we present a method for discovering the common salient objects from a set of images. We treat co-saliency detection as a pairwise saliency propagation problem, which utilizes the similarity between each pair of images to measure the common property with the guidance of a single saliency map image. Given the pairwise co-salient foreground maps, pairwise saliency is optimized by combining the initial background cues. Pairwise co-salient maps are then fused according to a novel fusion strategy based on the focus of human attention. Finally we adopt an integrated multi-scale scheme to obtain the pixel-level saliency map. Our proposed model makes the existing single saliency model perform well in co-saliency detection and is not overly sensitive to the initial saliency model selected. Extensive experiments on two benchmark databases show the superiority of our co-saliency model against the state-of-the-art methods both subjectively and objectively.