Leveraging local neighborhood topology for large scale person re-identification
作者：
Highlights：
• Semi-supervised approach that leverages all available imagery for re-identification.
• Brings the advantages of structured re-identification to unstructured problems.
• State-of-the-art performance on standard datasets.
• Results on re-identification problems with more than 30 000 images.
• Performance improves with more images, unlike other techniques.
摘要
Highlights•Semi-supervised approach that leverages all available imagery for re-identification.•Brings the advantages of structured re-identification to unstructured problems.•State-of-the-art performance on standard datasets.•Results on re-identification problems with more than 30 000 images.•Performance improves with more images, unlike other techniques.
论文关键词：Re-identification,Conditional random field,Semi-supervised,ETHZ,CAVIAR,3DPeS,CMV100
论文评审过程：Received 8 December 2013, Revised 3 April 2014, Accepted 7 June 2014, Available online 17 June 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.06.003
