Optimizing multi-graph learning based salient object detection

In this paper, we propose a novel bottom-up saliency detection algorithm to effectively detect salient objects. Different from most existing methods that are not robust to complex scenes, we utilize multi-graph learning to take various scenes into consideration. First, multiple features are used to represent superpixels, and then measured by multiple distance metrics to construct multiple graphs. The motivation is to take advantage of their complementary information to cope with different environments. Second, fixation and boundary cues are respectively used as foreground and background seeds. The fixation is effective for crowded backgrounds because of the observation that regions within eye fixations are very likely the image foreground. Third, we integrate the multiple graphs and seeds into a regularized and optimized multi-graph based learning framework to effectively generate foreground-based and background-based saliency maps. Finally, we integrate these two saliency maps to obtain a more smooth and accurate saliency map. Extensive experiments are conducted on five benchmark datasets. Experimental results show that the proposed bottom-up saliency detection method yields comparable or better results against the state-of-the-art methods, and is robust to both cluttered and clean scenes.