Person re-identification based on multi-region-set ensembles

Wei Li, Chao Huang, Bing Luo, Fanman Meng, Tiecheng Song, Hengcan Shi

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)


Person re-identification is an important topic in video surveillance. We present a new feature representation for person re-identification based on multi-region-set ensembles (MRSE) by combining some semantic regions with their contextual information. The motivation of this paper is that people can recognize and identify whether it's the same person by one or several local regions of the appearance. Our approach is divided into three steps: firstly, we segment the person into some semantic regions such as "hair", "face", "up-cloth" (upper clothes) and "lo-cloth" (lower clothes). After getting these regions, we form multiple sets of different combination by selecting a few regions and concatenating the features of them. We then combine the distance of all multiple sets for computing the similarity of a query-gallery image pair. Finally, we achieve higher rank-1 recognition rate and competitive performance compared with the state-of-the-art on iLIDS, VIPeR, CAVIAR4REID, 3DPeS four challenging datasets.

Original languageEnglish
Pages (from-to)67-75
Number of pages9
JournalJournal of Visual Communication and Image Representation
Issue numberPart A
Publication statusPublished - Oct 2016
Externally publishedYes


  • Feature representation
  • Person re-identification
  • Video surveillance

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