access icon free Large margin relative distance learning for person re-identification

© The Institution of Engineering and Technology
Received 07/08/2016, Accepted 07/04/2017, Revised 14/03/2017, Published 27/04/2017

Distance metric learning has achieved great success in person re-identification. Most existing methods that learn metrics from pairwise constraints suffer the problem of imbalanced data. In this study, the authors present a large margin relative distance learning (LMRDL) method which learns the metric from triplet constraints, so that the problem of imbalanced sample pairs can be bypassed. Different from existing triplet-based methods, LMRDL employs an improved triplet loss that enforces penalisation on the triplets with minimal inter-class distance, and this leads to a more stringent constraint to guide the learning. To suppress the large variations of pedestrian's appearance in different camera views, the authors propose to learn the metric over the intra-class subspace. The proposed method is formulated as a logistic metric learning problem with positive semi-definite constraint, and the authors derive an efficient optimisation scheme to solve it based on the accelerated proximal gradient approach. Experimental results show that the proposed method achieves state-of-the-art performance on three challenging datasets (VIPeR, PRID450S, and GRID).

Inspec keywords: pedestrians; image matching; learning (artificial intelligence); gradient methods

Other keywords: imbalanced data; proximal gradient approach; imbalanced sample pairs; large margin relative distance learning; person reidentification; triplet constraints; LMRDL; logistic metric learning problem; optimisation scheme; distance metric learning

Subjects: Image recognition; Optimisation techniques; Optimisation techniques; Knowledge engineering techniques; Computer vision and image processing techniques

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