基于散度-形状引导和优化函数的显著性目标检测

In order to detect saliency object accurately, this paper proposed an efficient framework for saliency detection based on scatter-shape guidance and optimization function. First, it proposed a discriminative similar metric by taking color, spatial and edge information into consideration. Based on similar metric together with background set obtained by removing the foreground noise in the image boundaries with scatter-guided, it constructed a background based saliency map. In order to improve the quality of detection, it introduced the shape completeness cue to generate the corresponding shape completeness saliency map by measuring the completeness of a region by the expectation of times for which it bounded the region by completely shape over the hierarchical space. Finally, it achieved the final saliency map by integrating the above both maps jointly into an optimization function. Quantitative experiments on four available datasets ASD, DUT-OMRON and ECSSD demonstrate that the proposed method outperforms other state-of-the-art approaches and detects the salient object which locates at random positions.