Person Re-Identification Based on Kernel Large Margin Nearest Neighbor Classification

被引：0

作者：

Yang, Linlin ^{[1
]}

Cheng, Jian ^{[1
]}

Liu, Haijun ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Elect Engn, 2006 Xiyuan Ave, Chengdu 611731, Sichuan, Peoples R China

来源：

COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS | 2018年 / 423卷

基金：

美国国家科学基金会;

关键词：

Person re-identification; Kernel trick; KLMNN;

D O I：

10.1007/978-981-10-3229-5_84

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Person re-identification is a process of matching person images of same identity across nonoverlapping camera views at different locations and times. In this paper, we introduce how to use the kernel trick to improve the performance of large margin nearest neighbor (LMNN) classification for person re-identification. Since the classification ability of LMNN is weak for those person features with nonlinear distribution, KLMNN combining kernel trick and LMNN is introduced to extend linear distance metric to nonlinear cases. Three kernel-based methods and two indicators are applied to evaluate the performance of KLMNN.

引用

页码：783 / 791

页数：9

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