针对已有显著性目标检测在单一先验知识下生成的显著图存在背景抑制不彻底、孤立背景块干扰及前景区域缺失的问题,提出复合先验的显著性目标检测方法。先利用超像素分割算法提取边缘超像素,构建初选背景集,根据边界和四个角落显著性均值优化背景集;针对背景超像素渐变性不强的特点,提出特征差异法;再构建粗略包围前景区域的凸包,将其质心位置设为中心位置;最后将三种先验显著图自适应权重融合,获得最终显著图。利用所提方法对MSRA和ESSCD数据集中的图像进行实验,结果表明,所提方法融合三种先验知识能够解决提出的问题,在抑制背景的同时,又能获得前景区域完整度较高的显著图。

In this study, we propose a complex prior saliency target detection method to solve the problems associated with the saliency maps. These problems are as follows: the saliency maps generated via the existing saliency target detection method under single prior knowledge exhibit incomplete background suppression, isolated background block interference, and lack of foreground area. First, the superpixel segmentation algorithm was applied to extract the edge superpixels, and the primary background set was constructed. Subsequently, the background set was optimized in accordance with the significance of the boundary and the four corners. Then, we proposed the feature difference method with respect to the characteristics of the background superpixels exhibiting a low gradient. Second, a convex hull, which roughly surrounds the foreground area, was constructed and set as the center position of its centroid. Finally, three prior saliency maps were adaptively weighted to obtain the final saliency map. The proposed method was used to perform experiments on the maps obtained using the MSRA and ESSCD datasets. The obtained results prove that the proposed method can solve the aforementioned problems by fusing three types of prior knowledges. It can simultaneously reduce background suppression and obtain a saliency map with the foreground area integrity of a significant degree.