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

• We propose a simple and effective strategy to combine semantic regions with their contextual information.
• We achieve higher rank-1 recognition rate and competitive performance compared with the state-of-the-art on four challenging datasets.

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.