Saliency Detection via Multi-view Synchronized Manifold Ranking

Saliency detection is an important problem in computer vision. Recently, graph-based manifold ranking (GMR) has been successfully employed in image saliency detection problem. Traditional GMR involves two main ranking stages, i.e., ranking with background queries and ranking with foreground queries. However, these two ranking stages are conducted separately which obviously ignores the correlation between background and foreground queries. Also, traditional GMR uses a single graph which lacks of considering multi-view features. To overcome these problems, in this paper, we propose a new multi-view synchronized manifold ranking for saliency detection problem. Our method aims to perform background and foreground ranking simultaneously by exploiting multiple kinds of features and thus performs more robustly and discriminatively for saliency detection problem. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed saliency detection method.

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