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Transfer Subspace Learning based on Double Relaxed Regression for Image Classification

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Abstract

A novel method based on relaxed regression and transfer subspace learning for cross-resolution image classification is presented. Firstly, a transfer subspace learning based on the double relaxed regression (TSL_DRR) method is adopted to learn a discriminative model and simultaneously avoid over-fitting in a regression-based classification task. Secondly, the matching efficiency between low-resolution face and high-resolution face is not ideal, so a so-called transfer subspace learning (TSL) technique is introduced to the proposed method to ensure that the domain data can be better matched by projecting different resolution face images onto the common subspace. Lastly, the global data structure and local data structure can be reliably retained by applying the low-rank and sparse constraint matrices, which also reduces the noise to an extent. Extensive experiments on various real image data sets indicate that the proposed method is effective in 4.2 accuracy.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant U1504610, Grant 61971339 and Grant 61471161 and 61972057, in part by the Key Project of the Natural Science Foundation of Shaanxi Province under Grant 2018JZ6002, in part by the Scientific and Technological Innovation Team of Colleges under Grant 20IRTSTHN018 and in party by the Natural Science Foundations of Henan Province under Grant 202300410148.

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Authors and Affiliations

  1. Information Engineering College, Henan University of Science and Technology, Luoyang, China

    Yue Lu, Zhonghua Liu, Hua Huo & Chunlei Yang

  2. College of Electronics and Information, Xi’an Polytechnic University, Xi’an, China

    Kaibing Zhang

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  1. Yue Lu
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  2. Zhonghua Liu
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  3. Hua Huo
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  4. Chunlei Yang
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  5. Kaibing Zhang
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Correspondence to Zhonghua Liu.

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Lu, Y., Liu, Z., Huo, H. et al. Transfer Subspace Learning based on Double Relaxed Regression for Image Classification. Appl Intell 52, 16294–16309 (2022). https://doi.org/10.1007/s10489-022-03213-z

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