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Relaxed local preserving regression for image feature extraction

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Abstract

The latest linear least regression (LSR) methods improved the performance of image feature extraction effectively by relaxing strict zero-one labels as slack forms. However, these methods have the following three disadvantages: 1) LSR-based methods are sensitive to the noises and may lose effectiveness in feature extraction task; 2) they only focus on the global structures of data, but ignore locality which is important to improve the performance; 3) they suffer from small-class problem, which means the number of projections learned by methods is limited by the number of classes. To address these problems, we propose a novel method called Relaxed Local Preserving Regression (RLPR) for image feature extraction. By incorporating the relaxed label matrix and similarity graph-based regularization term, RLPR can not only explore the latent structure information of data, but also solve the small-class problem. In order to enhance the robustness to noises, we further proposed an extended version of RLPR based on l2, 1-norm, termed as ERLPR. The experimental results on image databases consistently show that the recognition rates of RLPR and ERLPR are superior to the compared methods and can achieve 98% in normal cases. Especially, even on the corrupted databases, the proposed methods can also achieve the classification accuracy of more than 58%.

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Acknowledgments

This work was supported in part by the Natural Science Foundation of China (Grant 61802267, Grant 61773328, Grant 61732011 and Grant 61703283), and in part by the Shenzhen Municipal Science and Technology Innovation Council under Grant JCYJ20180305124834854 and JCYJ20190813100801664.

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Authors and Affiliations

  1. The College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Jiaqi Bao, Zhihui Lai & Xuechen Li

  2. Institute of Textiles & Clothing, The Hong Kong Polytechnic University, Hong Kong, China

    Zhihui Lai

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  1. Jiaqi Bao
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  2. Zhihui Lai
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  3. Xuechen Li
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Correspondence to Zhihui Lai.

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Bao, J., Lai, Z. & Li, X. Relaxed local preserving regression for image feature extraction. Multimed Tools Appl 80, 3729–3748 (2021). https://doi.org/10.1007/s11042-020-09802-9

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