This paper presents a co-saliency detection algorithm based on clustering and diffusion process. For each image in a set, intra saliency maps are constructed from the measure of boundary and contrast priors. Then, segmented regions of all images are clustered based on features of colors, intra saliency and coherence of saliency. Co-saliency of each cluster is computed from combination of foreground probability and coherence of the cluster. The co-saliency of cluster is propagated over the segmented regions according to affinity between the cluster and segments. In addition, we adopt an intra image diffusion process from a graph with learned fully affinity in order to improve spatial consistency of co-saliency maps. Experimental results show that our algorithm yields better results compared to the state-of-the-art methods in terms of precision-recall curve, visual plausibility and computational cost.
['Insung Hwang', 'Jae Sung Park', 'Gu Yong Park', 'Nam Ik Cho']
Image co-saliency detection based on clustering and diffusion process