Foreground Object Sensing for Saliency Detection

Hengliang Zhu, Bin Sheng, Xiao Lin, Yangyang Hao, Lizhuang Ma

Abstract: Many state-of-the-art saliency detection algorithms rely on the boundary prior, but these algorithms simply suppose the boundaries around an image as background regions. Here we propose a fast and effective algorithm for salient object detection. First, a novel method is proposed to approximately locate the foreground object by using the convex hull from Harris corner. On this basis, we divide the saliency values of different regions into two parts and generate the corresponding cue maps (foreground and background), which are combined into a convex hull prior map. Then a new prior based on distance to the convex hull center is proposed to replace the center prior. Finally, the convex hull prior map and the convex hull center-biased map are combined to be the saliency map, which is then optimized to get the final result. Compared with eighteen existing algorithms and tested on several datasets, the present algorithm performs well in terms of precision and recall.