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VOL. 1, ISSUE 1 (2025)
Content-based image retrieval systems: Technologies, challenges, and future directions
Authors
Ritwik Tiwari
Abstract
Content-Based Image Retrieval (CBIR) systems
represent a fundamental advancement in digital image management and search
technologies. These systems enable automatic retrieval of images from large
databases based on visual content rather than textual annotations, addressing
the semantic gap between low-level visual features and high-level human
perception. This analysis examines the core components of CBIR systems,
including feature extraction techniques, similarity measures, and indexing
strategies. We explore traditional approaches such as color histograms, texture
descriptors, and shape features, alongside modern deep learning methodologies
that have revolutionized the field. The paper discusses persistent challenges
including the semantic gap, scalability issues, and user interaction
complexities. Contemporary applications span from medical imaging and digital
libraries to e-commerce and social media platforms. We analyze recent
developments in neural networks, attention mechanisms, and multi-modal
approaches that integrate textual and visual information. The review concludes
with emerging trends including explainable AI in image retrieval, federated
learning approaches, and the integration of large vision-language models that
promise to further bridge the semantic gap in visual search systems.
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Pages:13-18
How to cite this article:
Ritwik Tiwari "Content-based image retrieval systems: Technologies, challenges, and future directions". World Journal of Engineering, Vol 1, Issue 1, 2025, Pages 13-18
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