Salient Object Detection Based on Unified Convex Surrogate for Non-Convex Schatten-p Norm
文献类型：期刊论文
| 作者 | Li, Min2; Yang, Yu2; Xu, Long1 ; Xu, Chen2; Sun, Xiaoli2
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| 刊名 | IEEE ACCESS
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| 出版日期 | 2020 |
| 卷号 | 8页码:20171-20180 |
| 关键词 | Low rank and sparsity decomposition non-convex weighted matrix decomposition salient object detection |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2020.2969271 |
| 英文摘要 | Nuclear norm and l 1 norm are the common regularization in salient object detection. However, existing literatures show that these terms either 1) are very slow for large scale problems due to singular value decomposition (SVD) on full matrix in every iteration, or 2) over-penalize the large singular values. In this paper, we propose to use respectively the non-convex weighted Schatten-p quasi-norm and lp -norm (0 < p < 1) for characterizing background and salient object. By matrix factorization, the optimization process, associated with the alternating direction method of multiplier (ADMM), is based on a unified convex surrogate which is only required to handle some small size matrices. Simultaneously, the convergence of algorithm is analyzed and validated. Experimental results indicate the new method usually outperform the state-of-the-art methods. |
| WOS关键词 | ALGORITHM |
| 资助项目 | National Nature Science Foundation of China[61872429] ; National Nature Science Foundation of China[61772343] ; National Nature Science Foundation of China[61972264] ; National Nature Science Foundation of China[61572461] ; National Nature Science Foundation of China[11790305] ; National Nature Science Foundation of Guangdong province[2017A030313354] ; National Nature Science Foundation of Guangdong province[2017A030310628] ; National Nature Science Foundation of Guangdong province[2019A1515010894] ; Shenzhen Basis Research Project[JCYJ20180305125521534] ; Shenzhen Basis Research Project[JCYJ20170818091621856] ; Shenzhen Basis Research Project[JCYJ20170302144838601] ; Interdisciplinary Innovation Team of Shenzhen University |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000525390100028 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Nature Science Foundation of China ; National Nature Science Foundation of China ; National Nature Science Foundation of Guangdong province ; National Nature Science Foundation of Guangdong province ; Shenzhen Basis Research Project ; Shenzhen Basis Research Project ; Interdisciplinary Innovation Team of Shenzhen University ; Interdisciplinary Innovation Team of Shenzhen University ; National Nature Science Foundation of China ; National Nature Science Foundation of China ; National Nature Science Foundation of Guangdong province ; National Nature Science Foundation of Guangdong province ; Shenzhen Basis Research Project ; Shenzhen Basis Research Project ; Interdisciplinary Innovation Team of Shenzhen University ; Interdisciplinary Innovation Team of Shenzhen University ; National Nature Science Foundation of China ; National Nature Science Foundation of China ; National Nature Science Foundation of Guangdong province ; National Nature Science Foundation of Guangdong province ; Shenzhen Basis Research Project ; Shenzhen Basis Research Project ; Interdisciplinary Innovation Team of Shenzhen University ; Interdisciplinary Innovation Team of Shenzhen University ; National Nature Science Foundation of China ; National Nature Science Foundation of China ; National Nature Science Foundation of Guangdong province ; National Nature Science Foundation of Guangdong province ; Shenzhen Basis Research Project ; Shenzhen Basis Research Project ; Interdisciplinary Innovation Team of Shenzhen University ; Interdisciplinary Innovation Team of Shenzhen University |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/55056]  |
| 专题 | 中国科学院国家天文台 |
| 通讯作者 | Yang, Yu; Xu, Long |
| 作者单位 | 1.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100101, Peoples R China 2.Shenzhen Univ, Coll Math & Stat, Shenzhen Key Lab Adv Machine Learning & Applicat, Shenzhen 518060, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Min,Yang, Yu,Xu, Long,et al. Salient Object Detection Based on Unified Convex Surrogate for Non-Convex Schatten-p Norm[J]. IEEE ACCESS,2020,8:20171-20180. |
| APA | Li, Min,Yang, Yu,Xu, Long,Xu, Chen,&Sun, Xiaoli.(2020).Salient Object Detection Based on Unified Convex Surrogate for Non-Convex Schatten-p Norm.IEEE ACCESS,8,20171-20180. |
| MLA | Li, Min,et al."Salient Object Detection Based on Unified Convex Surrogate for Non-Convex Schatten-p Norm".IEEE ACCESS 8(2020):20171-20180. |
入库方式： OAI收割
来源：国家天文台
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