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A Visual Saliency-Based Approach for Content-Based Image Retrieval

Author

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  • Aamir Khan

    (Independent Researcher, India)

  • Anand Singh Jalal

    (GLA University, Mathura, India)

Abstract

During the past two decades an enormous amount of visual information has been generated; as a result, content-based image retrieval (CBIR) has received considerable attention. In CBIR the image is used as a query to find the most similar images. One of the biggest challenges in CBIR system is to fill up the “semantic gap,” which is the gap between low-level visual features and the high-level semantic concepts of an image. In this paper, the authors have proposed a saliency-based CBIR system that utilizes the semantic information of image and users search intention. In the proposed model, firstly a significant region is identified with the help of method structured matrix decomposition (SMD) using high-level priors that highlight the prominent area of the image. After that, a two-dimensional principal component analysis (2DPCA) is used as a feature, which is compact and effectively used for fast recognition. Experiment results are validated on different image dataset having an extensive collection of semantic classifications.

Suggested Citation

  • Aamir Khan & Anand Singh Jalal, 2021. "A Visual Saliency-Based Approach for Content-Based Image Retrieval," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(1), pages 1-15, January.
    • Handle: RePEc:igg:jcini0:v:15:y:2021:i:1:p:1-15
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