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A Color Moment CBIR with a Local Binary Pattern and an Oriented Gradient Histogram

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  • Yashaswini Sridhar

    (Cambridge Institute of Technology, India)

  • Hareesh Kumar Kandikere Basavarajaiah

    (Tata Consultancy Services, India)

  • Shruthi RangaSwamy

    (Happiest Minds, India)

Abstract

In response to the demand for quick retrieval from huge picture collections, Content-Based Image Retrieval (CBIR) has grown in prominence as a field of study. A CBIR technique that combines color, texture, and form features is proposed in this paper. By segmenting photos into areas and determining color moments for each, color characteristics may be extracted. Gray-Level Co-occurrence Matrices (GLCMs) are used to assess texture. Five Fourier Descriptors are used to represent shape features. The 1000 photos in 10 categories of the Corel-1k database are used to test the system, which is constructed using MATLAB. Metrics for recall and precision are used to assess performance. The outcomes demonstrate enhanced retrieval precision in comparison to current techniques for all ten image classes. Additional texture and color characteristics may be investigated in future research depending on the application.

Suggested Citation

  • Yashaswini Sridhar & Hareesh Kumar Kandikere Basavarajaiah & Shruthi RangaSwamy, 2025. "A Color Moment CBIR with a Local Binary Pattern and an Oriented Gradient Histogram," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 9(6), pages 28-36, November.
    • Handle: RePEc:epw:ejece0:v:9:y:2025:i:6:id:19734
    DOI: 10.24018/ejece.2025.9.6.734
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