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Person re-identification is generally divided into two part: first how torepresent a pedestrian by discriminative visual descriptors and second how tocompare them by suitable distance metrics. Conventional methods isolate thesetwo parts, the first part usually unsupervised and the second part supervised.The Bag-of-Words (BoW) model is a widely used image representing descriptor inpart one. Its codebook is simply generated by clustering visual features inEuclidian space. In this paper, we propose to use part two metric learningtechniques in the codebook generation phase of BoW. In particular, the proposedcodebook is clustered under Mahalanobis distance which is learned supervised.Extensive experiments prove that our proposed method is effective. With severallow level features extracted on superpixel and fused together, our methodoutperforms state-of-the-art on person re-identification benchmarks includingVIPeR, PRID450S, and Market1501.

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