Distance learning by mining hard and easy negative samples for person re-identification
作者：
Highlights：
• We have proposed a Hard and Easy Negative samples mining based Distance learning (HEND) approach for person re-identification.
• We have designed a symmetric triplet constraint for the proposed HEND approach.
• We have proposed a Projection based HEND (PHEND) approach, which simultaneously learns a projection matrix and a distance metric.
• We have conducted extensive experiments in this paper to evaluate our approaches.
摘要
•We have proposed a Hard and Easy Negative samples mining based Distance learning (HEND) approach for person re-identification.•We have designed a symmetric triplet constraint for the proposed HEND approach.•We have proposed a Projection based HEND (PHEND) approach, which simultaneously learns a projection matrix and a distance metric.•We have conducted extensive experiments in this paper to evaluate our approaches.
论文关键词：Distance learning,Symmetric triplet constraint,Negative samples division,Projection matrix,Person re-identification
论文评审过程：Received 22 November 2018, Revised 26 May 2019, Accepted 10 June 2019, Available online 11 June 2019, Version of Record 25 June 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.06.007
