Co-Saliency Detection via a General Optimization Model and Adaptive Graph Learning

Co-saliency detection is an important research problem, and has been widely used in computer vision area. One main challenge for co-saliency detection problem is how to explore both interactive information among different images and individual salient information within each image simultaneously in co-saliency estimation. In this paper, we propose a novel general optimization framework with adaptive graph learning for co-saliency estimation problem. The proposed model integrates multiple cues including background, and foreground priors, structural information of images, and image feature representation together to obtain a uniform, and accurate co-saliency estimation. One main benefit of the proposed co-saliency method is that it conducts co-saliency propagation, and prediction across different images while maintains the individual salient information of each image, which ensures the consistency, and communication across different images effectively in co-saliency estimation. To improve the accuracy of co-saliency estimation, we adaptively learn a neighborhood, and structured graph to conduct co-saliency propagation among superpixels. An effective optimization algorithm has been designed to seek the optimal solution for the proposed co-saliency optimization model. Experimental results on several widely used datasets show that our method outperforms some other related co-saliency detection methods.

Automated summary

Co-saliency detection is an important research problem in computer vision, and this paper proposes a novel optimization framework to address this issue by integrating multiple cues for accurate estimation.