Pairwise Linear Regression Classification for Image Set Retrieval

被引:52
作者
Feng, Qingxiang [1 ]
Zhou, Yicong [1 ]
Lan, Rushi [1 ]
机构
[1] Univ Macau, Dept Comp & Informat Sci, Ave Univ, Taipa, Macau, Peoples R China
来源
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2016年
关键词
APPROXIMATED NEAREST POINTS; FACE RECOGNITION; COLLABORATIVE REPRESENTATION;
D O I
10.1109/CVPR.2016.526
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes the pairwise linear regression classification (PLRC) for image set retrieval. In PLRC, we first define a new concept of the unrelated subspace and introduce two strategies to constitute the unrelated subspace. In order to increase the information of maximizing the query set and the unrelated image set, we introduce a combination metric for two new classifiers based on two constitution strategies of the unrelated subspace. Extensive experiments on six well-known databases prove that the performance of PLRC is better than that of DLRC and several state-of-the art classifiers for different vision recognition tasks: cluster-based face recognition, video-based face recognition, object recognition and action recognition.
引用
收藏
页码:4865 / 4872
页数:8
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