Maximum correntropy criterion for discriminative dictionary learning

Pengyi Hao, Sei Ichiro Kamata

研究成果

6 被引用数 (Scopus)

抄録

In this paper, a novel discriminative dictionary learning with pairwise constraints by maximum correntropy criterion is proposed for pair matching problem. Comparing with the conventional dictionary learning approaches, the proposed method has several advantages: (i) It can deal with the outliers and noises problem more efficiently during the reconstruction step. (ii) It can be effectively solved by half-quadratic optimization algorithm, and in each iteration step, the complex optimization problem can be reduced to a general problem that can be efficiently solved by feature-sign search optimization. (iii) The proposed method is capable of analyzing non-Gaussian noise to reduce the influence of large outliers substantially, resulting in a robust and discriminative dictionary. We test the performance of the proposed method on two applications: face verification on the challenging restricted protocol of Labeled Faces in the Wild (LFW) benchmark and face-track identification on a dataset with more than 7,000 face-tracks. Compared with the recent state-of-the-art approaches, the outstanding performance of the proposed method validates its robustness and discriminability.

本文言語English
ホスト出版物のタイトル2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
ページ4325-4329
ページ数5
DOI
出版ステータスPublished - 2013 12 1
イベント2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
継続期間: 2013 9 152013 9 18

出版物シリーズ

名前2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
国/地域Australia
CityMelbourne, VIC
Period13/9/1513/9/18

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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