@inproceedings{bc9bb2ad0c8840e0b7288621bbd60cb7,
title = "Person re-identification by manifold ranking",
abstract = "Existing person re-identification methods conventionally rely on labelled pairwise data to learn a task-specific distance metric for ranking. The value of unlabelled gallery instances is generally overlooked. In this study, we show that it is possible to propagate the query information along the unlabelled data manifold in an unsupervised way to obtain robust ranking results. In addition, we demonstrate that the performance of existing supervised metric learning methods can be significantly boosted once integrated into the proposed manifold ranking-based framework. Extensive evaluation is conducted on three benchmark datasets.",
keywords = "distance metric learning, manifold, person re-identification, ranking, video surveillance",
author = "Loy, \{Chen Change\} and Chunxiao Liu and Shaogang Gong",
year = "2013",
doi = "10.1109/ICIP.2013.6738736",
language = "English",
isbn = "9781479923410",
series = "2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings",
publisher = "IEEE Computer Society",
pages = "3567--3571",
booktitle = "2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings",
address = "United States",
note = "2013 20th IEEE International Conference on Image Processing, ICIP 2013 ; Conference date: 15-09-2013 Through 18-09-2013",
}