Towards fast and kernelized orthogonal discriminant analysis on person re-identification
作者：
Highlights：
• We propose to solve the singularity problem in person re-identification by learning an orthogonal transformation with the pseudo-inverse of the within-class scatter matrix.
• We develop a kernel version for learning the orthogonal transformation against the non-linear distribution of data in person re-identification, thereby boosting the performance of person re-identification.
• We present a fast version with the unchanged performance of person reidentification for improving the solving efficiency.
• We conduct experiments on four challenging datasets to demonstrates the validity and advantage of the proposed method for solving the singularity problem in person re-identification, and analyze the effectiveness of both kernel version and fast version.
摘要
•We propose to solve the singularity problem in person re-identification by learning an orthogonal transformation with the pseudo-inverse of the within-class scatter matrix.•We develop a kernel version for learning the orthogonal transformation against the non-linear distribution of data in person re-identification, thereby boosting the performance of person re-identification.•We present a fast version with the unchanged performance of person reidentification for improving the solving efficiency.•We conduct experiments on four challenging datasets to demonstrates the validity and advantage of the proposed method for solving the singularity problem in person re-identification, and analyze the effectiveness of both kernel version and fast version.
论文关键词：Person re-identification,Metric learning,Singularity problem,Orthogonal discriminant analysis
论文评审过程：Received 6 May 2018, Revised 1 April 2019, Accepted 26 May 2019, Available online 28 May 2019, Version of Record 29 May 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.05.035
