A Novel Visual Word Co-occurrence Model for Person Re-identification

  • Ziming ZhangEmail author
  • Yuting Chen
  • Venkatesh Saligrama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8927)


Person re-identification aims to maintain the identity of an individual in diverse locations through different non-overlapping camera views. The problem is fundamentally challenging due to appearance variations resulting from differing poses, illumination and configurations of camera views. To deal with these difficulties, we propose a novel visual word co-occurrence model. We first map each pixel of an image to a visual word using a codebook, which is learned in an unsupervised manner. The appearance transformation between camera views is encoded by a co-occurrence matrix of visual word joint distributions in probe and gallery images. Our appearance model naturally accounts for spatial similarities and variations caused by pose, illumination & configuration change across camera views. Linear SVMs are then trained as classifiers using these co-occurrence descriptors. On the VIPeR [1] and CUHK Campus [2] benchmark datasets, our method achieves 83.86% and 85.49% at rank-15 on the Cumulative Match Characteristic (CMC) curves, and beats the state-of-the-art results by 10.44% and 22.27%.


Visual Word Appearance Model Reproduce Kernel Hilbert Space Camera View Codebook Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ziming Zhang
    • 1
    Email author
  • Yuting Chen
    • 1
  • Venkatesh Saligrama
    • 1
  1. 1.Boston UniversityBostonUSA

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