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, this paper introduced the shape completeness and generated the corresponding shape completeness saliency map by measuring the expectation of times of a region which was activated 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.

[2019-08-05] Printed Article /doc/2018.03.0188/v2