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Improving Performance of Colour-Histogram-Based CBIR Using Bin Matching for Similarity Measure

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12239))

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Abstract

Content-Based Image Retrieval (CBIR) systems work by searching huge databases for similar images that match a query image. The CBIR systems depend on computing similarity between two images to retrieve images of interest. The choice of suitable similarity measuring tool is key for effective and efficient retrieval of images. Predominantly similarity metrics such as Euclidean, Manhattan, City block distances among others are used extensively to compute the images similarity measure when the conventional colour histogram is used for image indexing. However, each of these similarity metrics suffers from issues of non-similar images having the same histogram and outliers in the distribution of colour content in images. In this paper, a proposed bin-by-bin inspections and classification for the measurement of similarity is presented. The approach distinguishes between the queried image and the target image, to obtain a more robust outcome. The outcomes stood superior to other state-of-the-art similarity metrics in respect to retrieval accuracy.

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Acknowledgement

This work is supported by the Sichuan Provincial Health and Family Planning Commission of China under the name Establishment and practice of an auxiliary intelligent decision making system for tumor patient evaluation. Grant number: 19ZDYF.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, 611731, China

    Martey Ezekiel Mensah, Xiaoyu Li & Hang Lei

  2. University of Energy and Natural Resources, P.O. Box 214, Sunyani, Ghana

    Appiah Obed & Ninfaakanga Christopher Bombie

Authors
  1. Martey Ezekiel Mensah
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  2. Xiaoyu Li
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  3. Hang Lei
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  4. Appiah Obed
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  5. Ninfaakanga Christopher Bombie
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Corresponding author

Correspondence to Martey Ezekiel Mensah .

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Editors and Affiliations

  1. Nanjing University of Information Science, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Mensah, M.E., Li, X., Lei, H., Obed, A., Bombie, N.C. (2020). Improving Performance of Colour-Histogram-Based CBIR Using Bin Matching for Similarity Measure. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_52

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  • DOI: https://doi.org/10.1007/978-3-030-57884-8_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57883-1

  • Online ISBN: 978-3-030-57884-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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