Pairwise Linear Regression Classification for Image Set Retrieval

Qingxiang Feng, Yicong Zhou, Rushi Lan; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4865-4872

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Feng_2016_CVPR,
author = {Feng, Qingxiang and Zhou, Yicong and Lan, Rushi},
title = {Pairwise Linear Regression Classification for Image Set Retrieval},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}