Salient Object Detection via Trace Representation and Regularization

被引：0

作者：

Ma, Xiaodi ^{[1
,2
]}

Wu, Xiyin ^{[1
,2
]}

Jin, Zhong ^{[1
,2
]}

机构：

[1] School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing,210094, China

[2] Key Laboratory of Intelligent Perception and System for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing,210094, China

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2018年 / 30卷 / 11期

关键词：

Object detection;

D O I：

10.3724/SP.J.1089.2018.17113

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Salient object detection intends to identify salient areas in natural images. According to low rank recovery theory, we propose a method via trace representation and regularization for salient object detection to separate the salient areas of the image from the background more completely. Firstly, a trace representation of matrix is used to obtain lower rank solution rather than the nuclear norm. Secondly, a Laplacian regularization is merged into model to reduce connection between sparse matrix and low-rank matrix. Finally, the color, location and boundary connectivity priors are integrated into a weight matrix, which is incorporated into the matrix decomposition model. Comparing with thirteen state-of-the-art methods in four challenging databases: MSRA1K, SOD, ECSSD and iCoseg, the experimental results based on Matlab show that our approach outperforms the state-of-the-art methods. © 2018, Beijing China Science Journal Publishing Co. Ltd. All right reserved.

引用

页码：2018 / 2025