A new graph ranking model for image saliency detection problem

Saliency detection is an important problem in many computer vision applications. As a kind of popular method, graph based manifold ranking (GMR) has been successfully used in saliency detection problem. In traditional GMR saliency detection, it involves two main stages, i.e., ranking with background queries and ranking with foreground queries. However, in GMR method, these two stages are conducted separately, which ignores the correlation between background and foreground cues. In this paper, we propose a new graph ranking model, which aims to perform background and foreground ranking simultaneously by exploiting the correlation between background and foreground cues. We derive a closed-form solution for it. Experimental results on four benchmark datasets demonstrate that the proposed method performs better than some other state-of-art methods.