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Thermal to Visual Person Re-Identification Using Collaborative Metric Learning Based on Maximum Margin Matrix Factorization
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DOI：10.1016/j.patcog.2022.109069.png)
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摘要
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Thermal to visual person re-identification (T2V-ReID) is a cross-domain image retrieval problem. In this problem, the matching of a person's image takes place, where the image is taken by different cameras (thermal and visual) at different times. This problem has numerous applications in night-time security surveillance. It is challenging due to the large intra-class variations and cross-domain discrepancies. Re-cently, deep metric learning methods are proposed for this problem. Still, there is a scope to improve the metric learning by generalizing the metric. In this paper, we have proposed the collaborative metric learning using Maximum Margin Matrix Factorization. It uses the group-wise similarities and collabo-ratively predicts the similarities. We can learn a more generalized metric by utilizing the maximized margin in this method. The proposed method is tested on the RegDB and RGB-D-T data sets, and the method outperforms the existing works in the few-shot learning settings. (c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Thermal to visual person re-identification
Cross-domain image retrieval
Collaborative metric learning
Matrix factorization
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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引用论文
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