Content-based image retrieval using deep learning and multidimensional indexing

Recent technological advancements and reductions in hardware and software costs have propelled visual search applications into the spotlight, making them both popular and indispensable. Consequently, the rapid and precise retrieval of images from vast databases through image queries has become a critical task. We introduce a novel end-to-end retrieval architecture that significantly enhances retrieval performance compared to a baseline system conducting database searches at the video frame level. Leveraging a pre-trained Convolutional Neural Network (CNN) model, we employ unsupervised image retrieval processes to extract and store low-level features for efficient indexing. To facilitate fast and efficient retrieval, we implement a tree-based indexing structure that leverages low-level features known as the Vantage Point Tree (VP Tree). To make these features compatible with our system, we employ dimension-reduction techniques to represent them in a lower-dimensional space. Our experiments, conducted on a benchmark image dataset, demonstrate that this approach leads to faster and more accurate retrieval when compared to a state-of-the-art search method known as K-Nearest Neighbor (KNN) search. Furthermore, we assess the proposed technique against KNN using two real-world video datasets, and it consistently outperforms KNN by a significant margin.