Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Metric Learning in Codebook Generation of Bag-of-Words for Person
  Re-identification   Person re-identification is generally divided into two part: first how to
represent a pedestrian by discriminative visual descriptors and second how to
compare them by suitable distance metrics. Conventional methods isolate these
two parts, the first part usually unsupervised and the second part supervised.
The Bag-of-Words (BoW) model is a widely used image representing descriptor in
part one. Its codebook is simply generated by clustering visual features in
Euclidian space. In this paper, we propose to use part two metric learning
techniques in the codebook generation phase of BoW. In particular, the proposed
codebook is clustered under Mahalanobis distance which is learned supervised.
Extensive experiments prove that our proposed method is effective. With several
low level features extracted on superpixel and fused together, our method
outperforms state-of-the-art on person re-identification benchmarks including
VIPeR, PRID450S, and Market1501.

13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio