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Similarity Learning with Top-heavy Ranking Loss for Person Re-identification.

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Bibliographic Details
Title: Similarity Learning with Top-heavy Ranking Loss for Person Re-identification.
Authors: Wang, Jin1, Sang, Nong1, Gao, Changxin1, Wang, Zheng2
Source: IEEE Signal Processing Letters. Jan2016, Vol. 23 Issue 1, p84-88. 5p.
Subjects: Structural learning theory, Constraints (Physics), Ranking (Statistics), Learning, Video surveillance
Abstract: Person re-identification is the task of finding a person of interest across a network of cameras. In this paper, we propose a new similarity learning method for person re-identification. Conventional metric learning methods generally learn a linear transformation by employing sparse pairwise or triplet constraints. Since a lot of negative matching pairs or triplets are abandoned, the discriminative information is not fully exploited. Similarity learning methods with AUC loss can utilize all valid triplet constraints. However, the AUC loss has its own limitation by treating all false ranks occured at different positions equally. To address this limitation, we propose to extend the AUC loss to the top-heavy ranking loss by assigning large weights to top positions of the ranking list. Moreover, we introduce an explicit nonlinear transformation function for the original feature space and learn an inner product similarity under the structured output learning framework. Our approach achieves very promising results on the challenging VIPeR, CUHK Campus and PRID 450S datasets. [ABSTRACT FROM PUBLISHER]
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Database: Engineering Source
Description
Abstract:Person re-identification is the task of finding a person of interest across a network of cameras. In this paper, we propose a new similarity learning method for person re-identification. Conventional metric learning methods generally learn a linear transformation by employing sparse pairwise or triplet constraints. Since a lot of negative matching pairs or triplets are abandoned, the discriminative information is not fully exploited. Similarity learning methods with AUC loss can utilize all valid triplet constraints. However, the AUC loss has its own limitation by treating all false ranks occured at different positions equally. To address this limitation, we propose to extend the AUC loss to the top-heavy ranking loss by assigning large weights to top positions of the ranking list. Moreover, we introduce an explicit nonlinear transformation function for the original feature space and learn an inner product similarity under the structured output learning framework. Our approach achieves very promising results on the challenging VIPeR, CUHK Campus and PRID 450S datasets. [ABSTRACT FROM PUBLISHER]
ISSN:10709908
DOI:10.1109/LSP.2015.2502271