Deep feature learning via structured graph Laplacian embedding for person re-identification

作者：

Highlights：

• This paper is the first to formulates the structured distance relationships into the graph Laplacian form for deep feature learning.

• Joint learning method is used in the framework to learn discriminative features.

• The results show clear improvements on public benchmark datasets and some are the state-of-the-art.

摘要

•This paper is the first to formulates the structured distance relationships into the graph Laplacian form for deep feature learning.•Joint learning method is used in the framework to learn discriminative features.•The results show clear improvements on public benchmark datasets and some are the state-of-the-art.

论文关键词：Person re-identification,Structured,Graph Laplacian,Deep learning

论文评审过程：Received 21 June 2017, Revised 12 April 2018, Accepted 5 May 2018, Available online 7 May 2018, Version of Record 15 June 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.05.007