  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.
