Person Re-identification by Multi-hypergraph Fusion | |
An, Le1; Chen, Xiaojing2; Yang, Songfan3; Li, Xuelong4; Chen, Xiaojing (xchen010@ucr.edu) | |
作者部门 | 光学影像学习与分析中心 |
2017-11-01 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
卷号 | 28期号:11页码:2763-2774 |
产权排序 | 4 |
摘要 | Matching people across nonoverlapping cameras, also known as person re-identification, is an important and challenging research topic. Despite its great demand in many crucial applications such as surveillance, person re-identification is still far from being solved. Due to drastic view changes, even the same person may look quite dissimilar in different cameras. Illumination and pose variations further aggravate this discrepancy. To this end, various feature descriptors have been designed for improving the matching accuracy. Since different features encode information from different aspects, in this paper, we propose to effectively leverage multiple off-the-shelf features via multi-hypergraph fusion. A hypergraph captures not only pairwise but also high-order relationships among the subjects being matched. In addition, different from conventional approaches in which the matching is achieved by computing the pairwise distance or similarity between a probe and a gallery subject, the similarities between the probe and all gallery subjects are learned jointly via hypergraph optimization. Experiments on popular data sets demonstrate the effectiveness of the proposed method, and a superior performance is achieved as compared with the most recent state-of-the-arts. |
文章类型 | Article |
关键词 | Feature Fusion Graph Learning Hypergraph Person Re-identification Surveillance |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TNNLS.2016.2602082 |
收录类别 | SCI ; EI |
关键词[WOS] | HYPERSPECTRAL IMAGE CLASSIFICATION ; FACE VERIFICATION ; CAMERA NETWORKS ; RECOGNITION ; FEATURES |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
项目资助者 | Fundamental Research Funds for the Central Universities(HUST 2016YXMS063) ; National Natural Science Foundation of China(61602193 ; 61501312) |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000413403900025 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28221 |
专题 | 光谱成像技术研究室 |
通讯作者 | Chen, Xiaojing (xchen010@ucr.edu) |
作者单位 | 1.Huazhong Univ Sci & Technol, Sch Automat, Natl Key Lab Sci & Technol Multispectral Informat, Wuhan 430074, Hubei, Peoples R China 2.Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 USA 3.Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610064, Sichuan, Peoples R China 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China |
推荐引用方式 GB/T 7714 | An, Le,Chen, Xiaojing,Yang, Songfan,et al. Person Re-identification by Multi-hypergraph Fusion[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(11):2763-2774. |
APA | An, Le,Chen, Xiaojing,Yang, Songfan,Li, Xuelong,&Chen, Xiaojing .(2017).Person Re-identification by Multi-hypergraph Fusion.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(11),2763-2774. |
MLA | An, Le,et al."Person Re-identification by Multi-hypergraph Fusion".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.11(2017):2763-2774. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Person Re-identifica(3831KB) | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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