A new patch selection method based on parsing and saliency detection for person re-identification

Yixiu Liu, Yunzhou Zhang, Sonya Coleman, Bir Bhanu, Shuangwei Liu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
27 Downloads (Pure)


Person re-identification is an important technique towards automatic recognition of a person across non- overlapping cameras. In this paper, a novel patch selection method based on parsing and saliency de- tection is proposed. The algorithm is divided into two stages. The first stage, primary selection: Deep Decompositional Network (DNN) is adopted to parse a pedestrian image into semantic regions, then slid- ing window and color matching techniques are proposed to select pedestrian patches and remove back- ground patches. The second stage, secondary selection: saliency detection is utilized to select reliable patches according to saliency map. Finally, PHOG, HSV and SIFT features are extracted from these patches and fused with the global feature LOMO to compensate for the inherent errors of saliency detection. By applying the proposed method on such datasets as VIPeR, PRID2011, CUHK01, CUHK03, PRID 450S and iLIDS-VID, it is found that the proposed descriptor can produce results superior to many state-of-the-art feature representation methods for person identification.
Original languageEnglish
Pages (from-to)86-99
Number of pages14
Early online date27 Sep 2019
Publication statusPublished - 21 Jan 2020


  • person re-identification
  • patch selection
  • pedestrian parsing
  • saliency detection
  • Feature fusion
  • Pedestrian parsing
  • Person re-identification
  • Patch selection
  • Saliency detection


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