iART: A Search Engine for Art-Historical Images to Support Research in the Humanities

Matthias Springstein, Stefanie Schneider, Javad Rahnama, Eyke Hüllermeier, Hubertus Kohle, Ralph Ewerth

Abstract: In this paper, we introduce iART: an open Web platform for art-historical research that facilitates the process of comparative vision. The system integrates various machine learning techniques for keyword- and content-based image retrieval as well as category formation via clustering. An intuitive GUI supports users to define queries and explore results. By using a state-of-the-art cross-modal deep learning approach, it is possible to search for concepts that were not previously detected by trained classification models. Art-historical objects from large, openly licensed collections such as Amsterdam Rijksmuseum and Wikidata are made available to users.