Unsupervised people organization and its application on individual retrieval from videos

Pengyi Hao, Sei Ichiro Kamata

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

In this paper, a method named histogram intersection metric learning from scene tracks is proposed for automatic organizing people in videos. We make the following contributions: (i) learning histogram intersection distance instead of Mahalanobis distance for widely used face features; (ii) learning the metric from scene tracks without manually labeling any examples, which enables learning across large variations in pose, expression, occlusion and illumination with small number of face pairs and can distinguish different people powerfully. We firstly test face identification, track clustering, and people organization on a long film, then individual retrieval based on people organization from a large video dataset is evaluated, demonstrating significantly increased search quality with respect to previous approaches on this area.

本文言語English
ホスト出版物のタイトルICPR 2012 - 21st International Conference on Pattern Recognition
ページ2001-2004
ページ数4
出版ステータスPublished - 2012 12 1
イベント21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
継続期間: 2012 11 112012 11 15

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
ISSN(印刷版)1051-4651

Conference

Conference21st International Conference on Pattern Recognition, ICPR 2012
国/地域Japan
CityTsukuba
Period12/11/1112/11/15

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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