This is the source code of our SPL paper:
The package contains the following components:
(1)/code
the source code for the proposed SLTRL method. It should be noted that the pca toolbox is downloaded from the homepage of Prof. Deng Cai.
(2)/demo
a demo for viper dataset. Please run LOMO_SLTRL.m to see the result.
(3)/cache
the extracted feature for each dataset. Here, we use LOMO [1] feature proposed by Prof. Shengcai Liao to conduct the experiment, please direct to his homepage to download the source code. (http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda/)
(4)/mat
train/test partition files for each dataset. The split is generated randomly, and the final reported result is the average of 10 random splits.
Version: 1.0 Date: 2015-11-17
Author: Jin Wang Institute: Huazhong University of Science and Techonology
Email: jinw@hust.edu.cn
References: [1] Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z. Li. “Person re-identification by local maximal occurrence representation and metric learning.” In IEEE Conference on Computer Vision and Pattern Recognition, 2015.