Saliency Detection via Multi-Center Convex Hull Prior
Zhijie Wang, Lizhuang Ma, Xiao Lin, Hui Zhong · 2018
Saliency detection has been a hot topic in computer vision. Among existing approaches, a representative one is to use the convex hull prior to find the salient object in the image; and there are many variants that are based on the convex hull prior. Most of these works used a single center to construct the convex hull center prior map, while few attention has been made on the use of multiple centers. In this paper, we propose a multi-center convex hull prior based solution for saliency detection. Particularly, our solution also integrates two non-trivial optimizations: one is for obtaining an enhanced global color distinction prior map, and another is for refining the preliminary saliency map. We experimentally evaluate our solution through comparing against state-of-the-art algorithms. The results demonstrate the effectiveness and superiorities of the proposed solution.