Feedback mechanism based iterative metric learning for person re-identification | |
Ren, Yutao1,2; Li, Xuelong2; Lu, Xiaoqiang2 | |
刊名 | PATTERN RECOGNITION |
2018-03-01 | |
卷号 | 75页码:99-111 |
关键词 | Person Re-identification Metric Learning Feedback Iteration |
ISSN号 | 0031-3203 |
DOI | 10.1016/j.patcog.2017.04.012 |
产权排序 | 1 |
英文摘要 | Person re-identification problem is targeting to match people in the views of non-overlapped camera networks. It is an important task in the fields of computer vision and video surveillance. It shows great value in applications like surveillance and action recognition. Existing metric learning based methods measure the similarity of sample pairs by learning a metric space in which the positive pairs are closer than negative pairs. However, the appearance features undergo with drastic variation. Person re-identification is a typical small sample problem. It is hard to learn a robust projection of metric subspace that takes all the situations into consideration. The learned metric subspace is usually over-fitting to training dataset due to the strict metric learning constraint. And the hard pairs in training dataset will weaken the discrimination of matching pairs' similarity. To address these problems, a feedback mechanism based iterative metric learning method is proposed. The proposed method introduces a mean distance of multi-metric subspace to deal with the over-fitting problem. The joint discriminant optimal model on feedback top ranks matching pairs will enhance the discrimination of matching pairs' similarity. It is a robust and discriminative distance metric which measures the matching pairs similarity with distances of multiple metric projections learned by a set of training datasets. Aiming to learn the multi-metric subspace, the proposed method gives a feedback mechanism based approach which back propagates the top ranks identification results as pseudo training datasets. The effectiveness of proposed mean distance of multi-metric projection is analyzed and proved theoretically. And extensive experiments on three challenging datasets, VIPeR, GRID and CUHK01 are conducted. The results show that the proposed method achieves the best performance and improves the state-of-the-art rank-1 identification rates by 18.48%, 2.00% and 5.41% on three datasets respectively. (C) 2017 Elsevier Ltd. All rights reserved. |
语种 | 英语 |
WOS记录号 | WOS:000418971900011 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/30815] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
作者单位 | 1.Wuhan Univ Technol, Wuhan 430070, Hubei, Peoples R China; 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China |
推荐引用方式 GB/T 7714 | Ren, Yutao,Li, Xuelong,Lu, Xiaoqiang. Feedback mechanism based iterative metric learning for person re-identification[J]. PATTERN RECOGNITION,2018,75:99-111. |
APA | Ren, Yutao,Li, Xuelong,&Lu, Xiaoqiang.(2018).Feedback mechanism based iterative metric learning for person re-identification.PATTERN RECOGNITION,75,99-111. |
MLA | Ren, Yutao,et al."Feedback mechanism based iterative metric learning for person re-identification".PATTERN RECOGNITION 75(2018):99-111. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论