Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification

Abstract

Person re-identification is generally divided into two part: the first is how to represent a pedestrian by discriminative visual descriptors and the second is how to compare them by suitable distance metrics. Conventional methods isolate these into two parts, the first part usually unsupervised and the second part supervised. The ag-of-Words (BoW) model is a widely used image representing descriptor in part one. Its codebook is simply generated by clustering visual features in Euclidean space, however, it is not optimal. In this paper, we propose to use a metric learning techniques of part two in the codebook generation phase of BoW. In particular, the proposed codebook is clustered under Mahalanobis distance which is learned supervised. Then local feature is compared with the codewords in the codebook by the trained Mahalanobis distance metric. Extensive experiments prove that our proposed method is effective. With several low level features extracted on superpixel and fused together, our method outperform state-of-the-art on person re-identification benchmarks including VIPeR, PRID 450S, and Market-1501.