Saliency Detection via Manifold Ranking Based on Robust Foreground  

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

机构地区：[1]Lanzhou Institute of Physics,China Academy of Space Technology,Lanzhou 730000,China

出　　处：《International Journal of Automation and computing》2021年第1期73-84,共12页国际自动化与计算杂志（英文版）

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 paper.First,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 map.Second,the feature points of the original image and the filtered image are obtained by using color boosting Harris corners to generate two different convex hulls.Calculating the intersection of these two convex hulls,a final convex hull is found.Finally,the foreground region and the final convex hull are combined to extract robust foreground seeds for manifold ranking and getting final saliency map.Experimental 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.

关 键 词：Saliency detection manifold ranking boundary connectivity convex hull robust foreground 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]