Image Database

Image databases are crucial for various computer vision tasks, aiming to efficiently store, retrieve, and analyze vast collections of images. Current research focuses on improving image retrieval methods, including novel algorithms for finding unique images, leveraging large multi-modal models for precise geolocalization, and employing techniques like contrastive learning and manifold factorization for robust data representation and efficient querying, even with noisy or incomplete data. These advancements are vital for applications ranging from cultural heritage preservation (through the creation of diverse image datasets) to improved search capabilities and the development of more accurate and robust computer vision systems.