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

Abstract

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 data set is built. To solve these problems, a novel relative distance metric leaning based on clustering centralization and projection vectors learning is proposed. When constructing training data set, the images of a same target person are clustering centralized with fuzzy c-means). The training data sets are built by these clusters in order to alleviate the imbalanced data problem of the training data sets. 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 data sets. 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.