Weighted Schatten p-norm and Laplacian scale mixture-based low-rank and sparse decomposition for foreground-background separation

被引：0

作者：

Fan, Ruibo ^{[1
]}

Jing, Mingli ^{[1
]}

Li, Lan ^{[2
]}

Shi, Jingang ^{[3
]}

Wei, Yufeng ^{[4
]}

机构：

[1] Xian Shiyou Univ, Sch Elect Engn, Xian, Peoples R China

[2] Xian Shiyou Univ, Sch Sci, Xian, Peoples R China

[3] Xi An Jiao Tong Univ, Sch Software Engn, Xian, Peoples R China

[4] Northwestern Polytech Univ, Sch Comp Sci, Xian, Peoples R China

来源：

JOURNAL OF ELECTRONIC IMAGING | 2023年 / 32卷 / 02期

基金：

中国国家自然科学基金;

关键词：

foreground-background separation; Laplacian scale mixture; low-rank approximation; sparse representation; weighted Schatten; p-norm; TRUNCATED NUCLEAR NORM; MOVING OBJECT DETECTION; ROBUST-PCA; MATRIX COMPLETION; SUBTRACTION;

D O I：

10.1117/1.JEI.32.2.023021

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Low-rank and sparse decomposition (LRSD) plays a vital role in foreground-background separation. The existing LRSD methods have the drawback: imprecise surrogate functions of rank and sparsity. We propose the weighted Schatten p-norm (WSN) and Laplacian scale mixture (LSM) method based on LRSD for foreground-background separation, which introduces the WSN and LSM to improve this drawback. To demonstrate the performance of the proposed method, it is applied to foreground-background separation and gets the highest average F-measure score.

引用

页数：11

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