Image salient region detection based on boundary expansion
LIU Jie1,2,3, WANG Shengjin1,2,3
1. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;
2. State Key Laboratory of Intelligent Technology and Systems, Beijing 100084, China;
3. Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China
Abstract：Background priors have been shown to improve salient region detection. Typically, image boundary patches are assumed to be the background and the saliency of other patches is defined by their difference from the boundaries. A greater difference indicates a more salient patch. However, when the background is cluttered, or the foreground overlaps the image boundary, using only boundary patches to indicate the background may lead to a saliency map with strong noise and compromise the detection accuracy. To address this problem, the boundary patches are first expanded here into the image interior to contain as much background as possible. Then, the rest of the patches are used as foreground queries with the saliency of each patch measured by a two-stage ranking algorithm. Tests on three large public datasets demonstrate the superiority of this method over five other algorithms.
刘杰, 王生进. 基于边界扩展的图像显著区域检测[J]. 清华大学学报（自然科学版）, 2017, 57(1): 72-78.
LIU Jie, WANG Shengjin. Image salient region detection based on boundary expansion. Journal of Tsinghua University(Science and Technology), 2017, 57(1): 72-78.
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2017, Vol. 57 
