ACTIVE LEARNING WITH RE-SAMPLING FOR SUPPORT VECTOR MACHINE IN PERSON RE-IDENTIFICATION

被引:0
|
作者
Xiang, Jin-Peng [1 ]
Bai, Yang [1 ]
机构
[1] S China Univ Technol, Sch Comp Sci & Engn, Machine Learning & Cybernet Res Ctr, Guangzhou 510006, Guangdong, Peoples R China
来源
PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOLS 1-4 | 2013年
关键词
Person re-identification; active learning; re-sampling; surveillance;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Person re-identification is defined as to find the same person who re-occurred in a multi-camera surveillance system. A classifier for person re-identification may suffer from the imbalance dataset problem since the number of the targeted images is much less than irrelevant images. In this paper, we proposed over-sampling and under-sampling method for the active learning method for person re-identification. The sampling method is activated when the imbalance level of the training set is higher than a preset value during iteration of the active learning. The effect of the imbalance problem is reduced. Experimental results show the active learning method with the proposed re-sampling method scarifies the true negative rate to achieve higher true positive rate, which is more important in person re-identification.
引用
收藏
页码:597 / 602
页数:6
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