Content Based Image Retrieval

Content-based image retrieval (CBIR) aims to find images visually similar to a query, overcoming limitations of text-based search. Current research emphasizes developing robust and efficient feature extractors, often leveraging deep learning architectures like transformers and autoencoders, and incorporating techniques such as metric learning, contrastive learning, and uncertainty quantification to improve retrieval accuracy and robustness across diverse image domains. CBIR's impact spans various fields, including medical imaging (improving diagnostics and research), e-commerce (enhancing product search), and remote sensing (facilitating efficient data analysis), with ongoing efforts focused on addressing challenges like the semantic gap and computational efficiency.