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

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

Aamir Khan, Anand Singh Jalal
Copyright: © 2021 |Volume: 15 |Issue: 1 |Pages: 15
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859826|DOI: 10.4018/IJCINI.2021010101
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MLA

Khan, Aamir, and Anand Singh Jalal. "A Visual Saliency-Based Approach for Content-Based Image Retrieval." IJCINI vol.15, no.1 2021: pp.1-15. http://doi.org/10.4018/IJCINI.2021010101

APA

Khan, A. & Jalal, A. S. (2021). A Visual Saliency-Based Approach for Content-Based Image Retrieval. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(1), 1-15. http://doi.org/10.4018/IJCINI.2021010101

Chicago

Khan, Aamir, and Anand Singh Jalal. "A Visual Saliency-Based Approach for Content-Based Image Retrieval," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.1: 1-15. http://doi.org/10.4018/IJCINI.2021010101

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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.