Abstract: Large-scale image indexing and retrieval are pivotal in artificial intelligence, especially within computer vision, for efficiently organizing and accessing extensive image databases. This systematic literature review employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to thoroughly analyze and synthesise the current research landscape in this domain. Through meticulous research and a stringent selection process, this study uncovers significant trends, pioneering methodologies, and ongoing challenges in large-scale image indexing and retrieval. Key findings reveal a growing adoption of deep learning techniques, the integration of multimodal data to improve retrieval accuracy, and persistent challenges related to scalability and real-time processing. These insights offer a valuable resource for researchers and practitioners striving to enhance the efficiency and effectiveness of image indexing and retrieval systems.

Abdelkrim Saouabe, Said Tkatek, Hicham Oualla and Carlos SOSA Henriquez. “Large-Scale Image Indexing and Retrieval Methods: A PRISMA-Based Review”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150732

@article{Saouabe2024,
title = {Large-Scale Image Indexing and Retrieval Methods: A PRISMA-Based Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150732},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150732},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Abdelkrim Saouabe and Said Tkatek and Hicham Oualla and Carlos SOSA Henriquez}
}