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Template-centric deep linear discriminant analysis for visual representation

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Abstract

In some real-world visual recognition tasks, instances are generated according to certain standards, which should serve as references during instance recognition. In this paper, we propose a template-centric representation learning (TCRL) framework that uses these standards as templates during recognition. The TCRL framework aims to learn a feature space where each instance is closely centered around its own template and away from the other templates. Within TCRL framework, we propose a template-centric objective function and a template-centric LDA layer, comprising two concrete models TDCNN and TDLDA. Experiments show that our method is superior to other traditional classification methods. The code will be made public after acceptance.

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Data Availability

All data included in this study are available upon request by contact with the corresponding author.

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Acknowledgements

This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant B230201025, and in part by the Key Research and Development Program of Changzhou (Social Development) under Grant CE20225042.

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Authors and Affiliations

  1. College of Information Science and Engineering, Hohai University, 1915 Hohai Avenue, Changzhou, 213200, Jiangsu, China

    Zongkai Chai, Liantao Wang, Haowen Shi & Zhaohui Yuan

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  1. Zongkai Chai
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  2. Liantao Wang
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  3. Haowen Shi
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  4. Zhaohui Yuan
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Correspondence to Liantao Wang.

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Chai, Z., Wang, L., Shi, H. et al. Template-centric deep linear discriminant analysis for visual representation. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19589-8

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