关键词: 显著目标检测, 显著性, 背景先验, 流行排序, 仿射传播聚类

Abstract: Existing salient object detection algorithm based graph-based manifold ranking is less effective in detecting images with complex background due to its idealistic prior background assumption. This paper proposes an improved algorithm based upon affinity propagation clustering and graph-based manifold ranking. First, the background superpixels on the boundary is extracted according to their color contrast. And then the affinity propagation clustering algorithm is utilized to adaptively obtain the color clusters which are used to compute the saliency of object and background as queries in the manifold ranking. Finally, the salient map is determined by integrating multiscale saliency. This proposed algorithm is compared with nine state-of-the-art methods in terms of precision, recall, F-measure, PR curves, AUC values and visual effect on four popular and public datasets of ASD, ECSSD, DUTOMRON and SED2, and the experimental results show the improvements over the state-of-the-art methods.

Key words: salient object detection, saliency, background prior, manifold ranking, affinity propagation clustering