Saliency detection based on integration of boundary and soft-segmentation
Saliency detection based on integration of boundary and soft-segmentation
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DOI：
10.1109/icip.2012.6467052
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发表时间：
2012-09
期刊：
影响因子：
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通讯作者：
Jing Sun;Huchuan Lu;Shifeng Li
中科院分区：
文献类型：
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作者：
Jing Sun;Huchuan Lu;Shifeng Li
视觉显著区域的检测是计算机视觉中一个具有挑战性和重要意义的问题。本文提出了一种基于边界的先验映射和一种基于凸包的软分割方法来改进显著检测。首先，我们提出了利用边界信息来获得粗先验映射。在此基础上，提出了一种通过软分割改进的凸包形成观测似然图。最后，应用贝叶斯公式将这两种映射结合起来。在一个公开可用的数据库上的实验表明，我们的增强框架相对于最先进的算法表现出了良好的性能。
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.