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International Conference on Neural Information Processing

ICONIP 2020: Neural Information Processing pp 162–173Cite as

Bionic Vision Descriptor for Image Retrieval

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12532))

Abstract

Human visual system gets remarkable performance by processing low-level features. In the last decade, many descriptors have been proposed for feature extraction. However, fewer of them get satisfying performance with low-level features. Compared to high-level ones, low-level features make use of natural underlying elements like texture and they are extracted directly, which makes low-level features more efficient in image retrieval domains. In this paper, a new descriptor named Bionic Vision Descriptor (BVD), which is based on the principle of human visual system, is proposed. The descriptor fuses uniform low-level features extracted from color, texture and gradient elements. Moreover, matrix calculation and feature selection are utilized to accelerate the calculation of BVD. Experimental results show that our method outperforms other state-of-the-art traditional descriptors with less runtime and fewer initial dimensions on benchmark datasets.

This study was funded by National Natural Science Foundation of Peoples Republic of China(61672130, 61972064), The Fundamental Research Funds for the Central Universities(DUT19RC(3)012, DUT20RC(5)010) and LiaoNing Revitalization Talents Program(XLYC1806006).

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Authors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China

    Guangzhe Li, Shenglan Liu, Feilong Wang & Lin Feng

Authors
  1. Guangzhe Li
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  2. Shenglan Liu
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  3. Feilong Wang
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  4. Lin Feng
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Corresponding author

Correspondence to Shenglan Liu .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Li, G., Liu, S., Wang, F., Feng, L. (2020). Bionic Vision Descriptor for Image Retrieval. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12532. Springer, Cham. https://doi.org/10.1007/978-3-030-63830-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-63830-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63829-0

  • Online ISBN: 978-3-030-63830-6

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