Similarity learning with joint transfer constraints for person re-identification

作者：

Highlights：

• A novel similarity learning method under joint transfer constraints is proposed to learn a discriminative subspace with consistent data distributions.

• The mid-level features are introduced in by defining the reconstruction matrix, by an optimal function addressed via the inexact augmented Lagrange multiplier (IALM) algorithm.

• During the process of objective function solution for optimization problem, based on confinement fusion of multi-view and multiple sub-regions, and a solution strategy is proposed to solve the objective function using joint matrix transform.

摘要

•A novel similarity learning method under joint transfer constraints is proposed to learn a discriminative subspace with consistent data distributions.•The mid-level features are introduced in by defining the reconstruction matrix, by an optimal function addressed via the inexact augmented Lagrange multiplier (IALM) algorithm.•During the process of objective function solution for optimization problem, based on confinement fusion of multi-view and multiple sub-regions, and a solution strategy is proposed to solve the objective function using joint matrix transform.

论文关键词：Person re-identification,Feature extraction,Similarity learning

论文评审过程：Received 7 December 2018, Revised 4 June 2019, Accepted 17 August 2019, Available online 28 August 2019, Version of Record 3 September 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.107014