Saliency detection based on integration of boundary and soft-segmentation
Jing Sun, Huchuan Lu, Shifeng Li · 2012
Detection of the visual salient regions is a challenging and significant problem in computer vision. In this paper, we propose a boundary based prior map and a soft-segmentation based convex hull to improve the saliency detection. First, we present to utilize the boundary information to obtain the coarse prior map. Then a convex hull improved by soft-segmentation is proposed to form the observation likelihood map. Finally, the Bayes formula is applied to combine these two maps. Experiments on a publicly available database show that our augmented framework performs favorably against the state-of-the-art algorithms.