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arXiv · 2108.01542

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
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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.

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Matthias Springstein, Stefanie Schneider, Javad Rahnama, Eyke Hüllermeier, Hubertus Kohle, Ralph Ewerth. 2021-08-03. iART: A Search Engine for Art-Historical Images to Support Research in the Humanities. https://doi.org/10.1145/3474085.3478564

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