Large margin relative distance learning for person re-identification

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).