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Weber’s law based multi-level convolution correlation features for image retrieval

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Abstract

Weber’s law reveals the relationship between human perception and perceptual stimuli. Inspired by the theory, this paper designs a multi-level convolution correlation feature statistic method for image retrieval. Firstly, the difference between a central pixel and its neighbors is described by Weber’s law through computing the differential excitation of image. Then, a multi-level saliency map is obtained by binary transformation and convolution operation. Thirdly, to exploit spatial correlation information of the image, a pixels pair-wise correlation and hierarchy statistic model is constructed. Finally, all intermediate features are concatenated into one histogram, which includes salient color and texture features. Extensive experiments demonstrate the proposed method of this paper has excellent performance.

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Acknowledgements

This work was supported by Key Development Plan Project of the Science and Technology Department of Henan Province(No.212102210400, No.182102310034, No.182102210151), Key Science and Technology Research Project of the Education Department of Henan Province(No.20A520047), Zhoukou Normal University High-level Talents Research Funding Project (No.ZKNUC2018005), National Nature Science Foundation of China(No.61672130).

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Authors and Affiliations

  1. School of Computer Science and Technology, ZhouKou Normal University, Zhoukou, 466001, China

    LaiHang Yu, WenGang Zhou, Shi Dong & Khushnood Abbas

  2. Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    LaiHang Yu & NingZhong Liu

  3. School of Network Engineering, ZhouKou Normal University, Zhoukou, 466001, China

    Yu Fan

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  1. LaiHang Yu
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Correspondence to LaiHang Yu or NingZhong Liu.

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Yu, L., Liu, N., Zhou, W. et al. Weber’s law based multi-level convolution correlation features for image retrieval. Multimed Tools Appl 80, 19157–19177 (2021). https://doi.org/10.1007/s11042-020-10355-0

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