Manuscript received July 24, 2025; revised September 29, 2025; accepted October 11, 2025; published March 26, 2026.

Abstract—The efficient management and analysis of largescale digital image collections have become critical challenges across various domains, including photographic archives, e-commerce platforms, and social networks. The effectiveness of image retrieval systems heavily depends on the choice of indexing methods, which influence both the speed and accuracy of visual information retrieval. Among the most prominent approaches, Content-Based Image Retrieval (CBIR) has emerged as a key technique, leveraging digital signatures derived from visual attributes such as color, shape, and texture. This study introduces an innovative CBIR-based automatic image indexing framework designed to enhance not only indexing automation but also retrieval accuracy and computational efficiency. The proposed approach has been rigorously evaluated on multiple image databases and benchmarked against state-of-the-art methods. Experimental results demonstrate significant improvements in retrieval speed, precision, and scalability, making this method a promising solution for real-world applications. By optimizing image management systems in terms of reliability and efficiency, this approach has the potential to transform diverse fields, including digital archiving, online advertising, and social media analytics. The adoption of this advanced indexing paradigm enables businesses and institutions to handle evergrowing volumes of digital images more effectively while ensuring seamless access to relevant visual information for end-users.

Keywords—visual information retrieval, Content-Based Image Retrieval (CBIR), image processing, feature selection, vectorial similarity

Cite: Abdelkrim Saouabe and Said Tkatek, "Intelligent Image Indexing for Fast, Scalable, and Accurate Visual Information Retrieval," Journal of Image and Graphics, Vol. 14, No. 2, pp. 194-207, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC-BY-4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.