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Person Re-Identification by Manifold Ranking

6 years 3 months ago
Person Re-Identification by Manifold Ranking
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.
Chen Change Loy, Chunxiao Liu, Shaogang Gong
Added 16 May 2013
Updated 16 May 2013
Type Conference
Year 2013
Where ICIP
Authors Chen Change Loy, Chunxiao Liu, Shaogang Gong
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