计算机工程与应用2016，Vol.52Issue(22)：26-32,38,8.DOI:10.3778/j.issn.1002-8331.1605-0010
基于图的流行排序的显著目标检测改进算法
Improved salient object detection based upon graph-based manifold ranking
摘要
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.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Key words
salient object detection/saliency/background prior/manifold ranking/affinity propagation clustering分类
信息技术与安全科学引用本文复制引用
张晴,林家骏,戴蒙..基于图的流行排序的显著目标检测改进算法[J].计算机工程与应用,2016,52(22):26-32,38,8.基金项目
国家自然科学基金（No.61401281）；上海市自然科学基金（No.14ZR1440700）。 （）
