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Link to original content: https://doi.org/10.1007/s10489-023-05067-5
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Unsupervised person re-identification based on distribution regularization constrained asymmetric metric learning

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Abstract

In unsupervised person re-identification, the traditional asymmetric metric learning alleviates the bias of person images from different views. However, there still exists the issue that the features of the same person are not close to each other in the feature space after asymmetric metric learning. The main reason is that the algorithm cannot overcome other interference except the view, such as the change in person’s clothes and scenes, etc. Therefore, the traditional asymmetric metric learning still has the issue of distribution differences in feature space. To address this issue, we propose an asymmetric metric learning method based on distribution regularization constraints for unsupervised person re-identification. First, the JSTL technique pretrains the feature extraction network to obtain robust features. Then, a new asymmetric metric objective function is defined, that is, a distributed regularization constraint term is introduced into the traditional asymmetric metric learning objective function. This method not only alleviates the bias caused by different views, but also effectively overcomes the issue of low recognition accuracy caused by the interference of scenes and clothing changes other than views. Finally, the gradient descent method is used to optimize the objective function, and the optimal metric matrix is obtained by solving the generalized eigenvalue problem. Experiments are implemented on VIPeR, CUHK01, Market-1501, DukeMTMC-Reid, and MSMT17 datasets, and the results show that the Rank-1 values of the algorithm are 35.2\(\%\), 52.62\(\%\), 57.1\(\%\), 42.6\(\%\) and 24.5\(\%\). Extensive experimental results show that our algorithm gains significant improvement compared with the state-of-art unsupervised person re-identification algorithms.

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Acknowledgements

This work is supported by the Shandong provincial Natural Science Foundation (No.ZR2022MF307), the Shandong University of Technology and Zhangdian District (No.118228), the National Natural Science Foundation of China (No.61801272).

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  1. School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, 255049, China

    Yue Liu, Guofeng Zou, Guizhen Chen, Mingliang Gao & Liju Yin

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Liu, Y., Zou, G., Chen, G. et al. Unsupervised person re-identification based on distribution regularization constrained asymmetric metric learning. Appl Intell 53, 28879–28894 (2023). https://doi.org/10.1007/s10489-023-05067-5

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