Saliency detection via multi-view graph based saliency optimization

Abstract

Saliency detection is an important problem in computer vision and pattern recognition area. Many works have been proposed for addressing the saliency detection task. As a popular method, graph based saliency optimization has been widely studied. However, previous works have universally focussed on single graph optimization which fails to consider multi-view feature representation of image content. In this paper, we first provide a general framework for traditional graph based saliency optimization models. Then, we extend the general framework to the multi-view case and propose our general multi-view graph based saliency optimization model. Finally, we present a particular implementation of our general model and derive an effective updating algorithm to solve it. Experimental results using several benchmark datasets demonstrate the effectiveness of our proposed saliency model.