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Evaluation of RGB Quantization Schemes on Histogram-Based Content Based Image Retrieval

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Artificial Intelligence and Security (ICAIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12737))

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Abstract

Colour feature indexing for images has seen several approaches such as Conventional Colour Histogram, Colour Coherent Vector, Colour Moment and Colour Correlogram. These approaches for indexing images have proven to be fast, simple, and retrieve images from database with satisfactory results. The strength of these approaches however is based on the colour space and quantization schemes employed for the indexing of images. Various works have explored colour spaces and quantization for CBIR applications and have reported that the RGB colour space for CBIR sometime suffers some inefficiencies in retrieval accuracies. Interestingly, almost all the experiments used images that were converted from RGB colour space. Mathematical formulas were used to perform this conversion of RGB colour space to the other spaces. This suggests that RGB colour space may not necessarily be a poorer colour space for CBIR application, but the choice of quantization affects its performance with CBIR task. This work therefore evaluated various quantization schemes (uniform and non-uniform) to determine which of the schemes perform best for histogram based CBIR application. Results show that CBIR developers can opt for RGB quantization schemes in the combination of 4s and 8s bins on each of the colour channel or band for optimum retrieval.

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Acknowledgement

This work is supported by the National key Research & Development Program of China, Grant No. 2018YFÄ‚703.

Author information

Authors and Affiliations

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

    Ezekiel Mensah Martey, Hang Lei & Xiaoyu Li

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

    Obed Appiah & Nicodemus Songose Awarayi

Authors
  1. Ezekiel Mensah Martey
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  2. Hang Lei
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  3. Xiaoyu Li
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  4. Obed Appiah
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  5. Nicodemus Songose Awarayi
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Corresponding author

Correspondence to Ezekiel Mensah Martey .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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The authors declare that they have no known or potential competing financial interests that could have appeared to influence the work reported in this paper.

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Martey, E.M., Lei, H., Li, X., Appiah, O., Awarayi, N.S. (2021). Evaluation of RGB Quantization Schemes on Histogram-Based Content Based Image Retrieval. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12737. Springer, Cham. https://doi.org/10.1007/978-3-030-78612-0_60

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  • DOI: https://doi.org/10.1007/978-3-030-78612-0_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78611-3

  • Online ISBN: 978-3-030-78612-0

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