Saliency Detection via Manifold Ranking Based on Robust Foreground

作     者：Wei-Ping Ma Wen-Xin Li Jin-Chuan Sun Peng-Xia Cao Wei-Ping Ma;Wen-Xin Li;Jin-Chuan Sun;Peng-Xia Cao

作者机构：Lanzhou Institute of PhysicsChina Academy of Space TechnologyLanzhou 730000China 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2021年第18卷第1期

页      面：73-84页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

主　　题：Saliency detection manifold ranking boundary connectivity convex hull robust foreground 

摘      要：The graph-based manifold ranking saliency detection only relies on the boundary background to extract foreground seeds,resulting in a poor saliency detection result,so a method that obtains robust foreground for manifold ranking is proposed in this ***,boundary connectivity is used to select the boundary background for manifold ranking to get a preliminary saliency map,and a foreground region is acquired by a binary segmentation of the ***,the feature points of the original image and the filtered image are obtained by using color boosting Harris corners to generate two different convex *** the intersection of these two convex hulls,a final convex hull is ***,the foreground region and the final convex hull are combined to extract robust foreground seeds for manifold ranking and getting final saliency *** results on two public image datasets show that the proposed method gains improved performance compared with some other classic methods in three evaluation indicators:precision-recall curve,F-measure and mean absolute error.