Optimizing Content-Based Image Retrieval System Using Convolutional Neural Network Models

This study focuses on improving “Content-Based Image Retrieval” (CBIR) systems through the utilization of optimized convolutional neural network (CNN) models. Traditionally, CBIR relied on text-based approaches, which proved inefficient in the digital era. In this research, we propose a novel visual content-based retrieval method, leveraging key image features like color, texture, and shape. To expedite the process, we employ pre-trained CNN models, such as the ResNet architecture, trained on extensive datasets like Image-Net. Our experiments, conducted on the “Paris 6K and Oxford 5K datasets, demonstrate the superior performance” of the ResNet model compared to the state-of-the-art AlexNet. This is attributed to ResNet’s ability to reduce training time without sacrificing accuracy. The evaluation, based on average mean precision, yielded promising results of 92.12% for Paris 6K and 84.81% for Oxford 5K, showcasing the efficacy of our approach.