Saliency integration driven by similar images

This paper proposes a saliency integration approach via the use of similar images to elevate saliency detection performance. Given the input image, a group of similar images are first retrieved, and meanwhile, the corresponding multiple saliency maps of the input image are generated by using existing saliency models. Then, the saliency fusion map is generated by using an adaptive fusion method to integrate such saliency maps, for which the fusion weights are measured by the corresponding similarity between each similar image and the input image. Next, an inter-image graph, for each pair of input image and similar image, is constructed to propagate the confident saliency values from the similar image to the input image, yielding the saliency propagation map. Finally, the saliency fusion map and the saliency propagation map are integrated to obtain the final saliency map. Experimental results on two public datasets demonstrate that the proposed approach achieves the better saliency detection performance compared to the existing saliency models and other saliency integration approaches.