Person re-identification based on a novel mahalanobis distance feature dominated KISS metric learning

被引：4
|
作者：
Zeng, Mingyong [1 ]
Wu, Zemin [1 ]
Tian, Chang [1 ]
Zhang, Lei [1 ]
Zhao, Xiaolong [2 ]
机构：
[1] PLA Univ Sci & Technol, Coll Commun Engn, Nanjing 210007, Jiangsu, Peoples R China
[2] Unit 61541 PLA, Beijing 100094, Peoples R China
来源：
ELECTRONICS LETTERS | 2016年 / 52卷 / 14期
关键词：
D O I：
10.1049/el.2016.0290
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：
A novel and efficient method for person re-identification based on mahalabobis distance feature is proposed. This feature is built on the existing features but exhibits extraordinary discriminative power. With an additional lightweight PCA, it can be cascaded to greatly enhance the overall discrimination. Then Keep It Simple and Straightforward (KISS) metric learning is conducted on such enhanced features. The approach has been evaluated against current methods on a benchmark dataset and can reach outstanding performance.
引用
收藏
页码：1223 / 1224
页数：2
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