Low-Rank 2-D Neighborhood Preserving Projection for Enhanced Robust Image Representation | |
Lu, Yuwu1; Lai, Zhihui1,2; Li, Xuelong3,4,5![]() | |
作者部门 | 光谱成像技术研究室 |
2019-05 | |
发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS
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ISSN | 2168-2267;2168-2275 |
卷号 | 49期号:5页码:1859–1872 |
产权排序 | 5 |
摘要 | 2-D neighborhood preserving projection (2DNPP) uses 2-D images as feature input instead of 1-D vectors used by neighborhood preserving projection (NPP). 2DNPP requires less computation time than NPP. However, both NPP and 2DNPP use the L2 norm as a metric, which is sensitive to noise in data. In this paper, we proposed a novel NPP method called low-rank 2DNPP (LR-2DNPP). This method divided the input data into a component part that encoded low-rank features, and an error part that ensured the noise was sparse. Then, a nearest neighbor graph was learned from the clean data using the same procedure as 2DNPP. To ensure that the features learned by LR-2DNPP were optimal for classification, we combined the structurally incoherent learning and low-rank learning with NPP to form a unified model called discriminative LR-2DNPP (DLR-2DNPP). By encoding the structural incoherence of the learned clean data, DLR-2DNPP could enhance the discriminative ability for feature extraction. Theoretical analyses on the convergence and computational complexity of LR-2DNPP and DLR-2DNPP were presented in details. We used seven public image databases to verify the performance of the proposed methods. The experimental results showed the effectiveness of our methods for robust image representation. |
关键词 | 2-D neighborhood preserving projection (2DNPP) image representation low-rank robust feature extraction |
DOI | 10.1109/TCYB.2018.2815559 |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000460667400026 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/31330 |
专题 | 光谱成像技术研究室 |
通讯作者 | Lu, Yuwu |
作者单位 | 1.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518055, Peoples R China 2.Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Peoples R China 3.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China 4.Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China 5.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 6.Tsinghua Univ, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China 7.Hong Kong Polytech Univ, Biometr Res Ctr, Hong Kong, Peoples R China |
推荐引用方式 GB/T 7714 | Lu, Yuwu,Lai, Zhihui,Li, Xuelong,et al. Low-Rank 2-D Neighborhood Preserving Projection for Enhanced Robust Image Representation[J]. IEEE TRANSACTIONS ON CYBERNETICS,2019,49(5):1859–1872. |
APA | Lu, Yuwu,Lai, Zhihui,Li, Xuelong,Wong, Wai Keung,Yuan, Chun,&Zhang, David.(2019).Low-Rank 2-D Neighborhood Preserving Projection for Enhanced Robust Image Representation.IEEE TRANSACTIONS ON CYBERNETICS,49(5),1859–1872. |
MLA | Lu, Yuwu,et al."Low-Rank 2-D Neighborhood Preserving Projection for Enhanced Robust Image Representation".IEEE TRANSACTIONS ON CYBERNETICS 49.5(2019):1859–1872. |
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Low-Rank 2-D Neighbo(1718KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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