Relative Distance Metric Leaning Based on Clustering Centralization and Projection Vectors Learning for Person Re-identification

Existing projection-based person re-identification methods usually suffer from long time training, high dimension of projection matrix, and low matching rate. In addition, the intra-class instances may be much less than the inter-class instances when a training dataset is built. To solve these problems, a novel relative distance metric leaning based on clustering centralization and projection vectors learning (RDML-CCPVL) is proposed. When constructing training dataset, the images of a same target person are clustering centralized with FCM. The training datasets are built by these clusters in order to alleviate the imbalanced data problem of the training datasets. In addition, during learning projection matrix, the resulted projection vectors can be approximately orthogonal by using an iteration strategy and a conjugate gradient projection vector learning method to update training datasets. The advantage of this method is its quadratic convergence, which can promote the convergence. Experimental results show that the proposed algorithm has higher efficiency. The matching rate can be significantly improved, and the time of training is much shorter than most of existing algorithms of person re-identification.