A Hybrid CBIR Framework Using Vision Transformers and Genetic Algorithm for Enhanced Image Retrieval

Content-Based Image Retrieval (CBIR) is an essential tool for arranging and acquiring visual content from large-scale image databases. This research presents a robust hybrid CBIR structure that combines transformer-based deep feature extraction with Genetic Algorithm (GA) optimization to significantly improve retrieval accuracy and efficiency. The proposed system introduces Vision Transformers (ViT) to efficiently capture intricate, global visual figures over the distinctive image categories, supporting both single and multi-object image retrieval scenarios. By influencing the long-range dependency modelling abilities of transformers, the system extracts highly different feature representations. These elements are further optimized with the help of Genetic Algorithm, a powerful adaptive technique that efficiently enhances feature selection and matching through iterative selection, crossover, and mutation processes. Comprehensive experiments were performed on the Corel 1K benchmark dataset illustrates the proposed hybrid model surpasses conventional CBIR model in terms of precision, recall, accuracy, and F1-score. The system achieves a retrieval accuracy of 99.38%, an F1-score of 95.12%, and a reduced error rate of 0.62%, showcasing its superior retrieval performance and computational efficiency. The results highlight the potential of integrating transformer-based deep learning with evolutionary optimization in advancing modern CBIR systems.