Bagging based metric learning for person re-identification

Person re-identification is a challenging problem in computer vision due to large variations of appearance among different cameras. Recently, metric learning is widely used to model the transformation between cameras. However, traditional metric learning based methods only learn one metric for the whole feature space, which cannot model different kinds of appearance variations well. In this paper, we introduce bagging into metric learning, and propose a bagging-based large margin nearest neighbor (LMNN) method for person re-identification. That is, multiple LMNN predictors are generated on sub-regions of the feature space and leveraged to obtain an aggregated predictor for performance improvement. Two bagging strategies, sample-bagging and feature-bagging, are proposed and compared. Extensive experiments on three benchmarks demonstrate the superiority of proposed approach over state-of-the-art methods.