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Noise-insensitive image representation via multiple extended LDB and class supervised intelligent coordination feature selection

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Abstract

Image description plays a dominant role in the computer vision and pattern recognition fields and has achieved a remarkable success for noise free or low-noise image. However, the methods face a great challenge when suffering from noise. Feature extraction is divided into floating point and binary. However floating point feature descriptors are complex and inefficient. Local difference binary (LDB) is one of state-of-art methods for higher accuracy and speed. Motivated by LDB, we propose an improved LDB named as multiple extended LDB (MELDB) to improve the effectiveness of basic LDB descriptor via gradients and difference of gradient information in four directions and explore much more discriminative noise-invariant features. MELDB has strong robustness under complex illumination and noise conditions. Next, we improve GA and BPSO algorithms called as FDSGA and ALBPSO, respectively. To further improve the discriminative ability of MELDB, a novel feature selection method named as intelligent coordination feature selection (ICFS) is represented to select the optimized subset of MELDB feature, based on co-evolution of FDSGA and ALBPSO. Then, to fully utilize class information, we further promote the ICFS named as class supervised ICFS to select the optimal subset feature. Extensive experiments are conducted to verify the effectiveness and efficiency of proposed methods on CMUPIE, YALE B, YALE B Ext databases and those noisy databases. The results reflect that our methods outperform other state-of-the-art methods and show strong robustness on noise, especially under severe noise conditions.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This project is supported by the National Key R&D Program of China (2019YFE0108300); National Natural Science Foundation of China (52172379, 62001058, U1864204); Fundamental Research Funds for the Central Universities, CHD (300102241201, 300102242901).

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Authors and Affiliations

  1. School of Information Engineering, Chang’an University, Xi’an, 710064, China

    Xi Zhang, Ting Chen & Tao Gao

  2. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yongxiong Liu

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  1. Xi Zhang
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Correspondence to Ting Chen.

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Zhang, X., Liu, Y., Chen, T. et al. Noise-insensitive image representation via multiple extended LDB and class supervised intelligent coordination feature selection. J Supercomput 79, 3227–3256 (2023). https://doi.org/10.1007/s11227-022-04658-2

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