Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification

Person re-identification is becoming a hot research topic due to its value in both machine learning research and video surveillance applications. For this challenging problem, distance metric learning is shown to be effective in matching person images. However, existing approaches either require a heavy computation due to the positive semidefinite (PSD) constraint, or ignore the PSD constraint and learn a free distance function that makes the learned metric potentially noisy. We argue that the PSD constraint provides a useful regularization to smooth the solution of the metric, and hence the learned metric is more robust than without the PSD constraint. Another problem with metric learning algorithms is that the number of positive sample pairs is very limited, and the learning process is largely dominated by the large amount of negative sample pairs. To address the above issues, we derive a logistic metric learning approach with the PSD constraint and an asymmetric sample weighting strategy. Besides, we successfully apply the accelerated proximal gradient approach to find a global minimum solution of the proposed formulation, with a convergence rate of O(1/t^2) where t is the number of iterations. The proposed algorithm termed MLAPG is shown to be computationally efficient and able to perform low rank selection. We applied the proposed method for person re-identification, achieving state-of-the-art performance on four challenging databases (VIPeR, QMUL GRID, CUHK Campus, and CUHK03), compared to existing metric learning methods as well as published results.