CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
Towards fast and kernelized orthogonal discriminant analysis on person re-identification
Min Cao1,2; Chen Chen1,2; Xiyuan Hu1,2; Silong Peng1,2,3
Source PublicationPattern Recognition
2019
Issue94Pages:218-229
Abstract

Recognizing a person across different non-overlapping camera views, is the task of person re-identification. For achieving the task, an effective way is to learn a discriminative metric by minimizing the within-class variance and maximizing the between-class variance simultaneously. However, the dimension of sample feature vector is usually greater than the number of training samples, as a result, the within-class scatter matrix is singular and the metric cannot be learned. In this paper, we propose to solve the singularity problem by employing the pseudo-inverse of the within-class scatter matrix and learning an orthogonal transformation for the metric. The proposed method can be effectively solved with a closed-form solution and no parameters required to tune. In addition, we develop a kernel version against non-linearity in person re-identification, and a fast version for more efficient solution. In experiments, we prove the validity and advantage of the proposed method for solving the singularity problem in person re-identification, and analyze the effectiveness of both kernel version and fast version. Extensively comparative experiments on VIPeR, PRID2011, CUHK01 and CUHK03 person re-identification benchmark datasets, show the state-of-the-art results of the proposed method.

KeywordPerson Re-identificationmetric Learningsingularity Problemorthogonal Discriminant Analysis
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25782
Collection智能制造技术与系统研究中心_多维数据分析
个人空间
Corresponding AuthorChen Chen
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Beijing ViSystem Corporation Limited, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Min Cao,Chen Chen,Xiyuan Hu,et al. Towards fast and kernelized orthogonal discriminant analysis on person re-identification[J]. Pattern Recognition,2019(94):218-229.
APA Min Cao,Chen Chen,Xiyuan Hu,&Silong Peng.(2019).Towards fast and kernelized orthogonal discriminant analysis on person re-identification.Pattern Recognition(94),218-229.
MLA Min Cao,et al."Towards fast and kernelized orthogonal discriminant analysis on person re-identification".Pattern Recognition .94(2019):218-229.
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