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

Springstein, Matthias; Schneider, Stefanie ; Rahnama, Javad; Hüllermeier, Eyke ; Kohle, Hubertus und Ewerth, Ralph (October 2021): iART: A Search Engine for Art-Historical Images to Support Research in the Humanities. 29th ACM International Conference on Multimedia, Virtual, October 20 - 24, 2021. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 2801-2803

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.

• Item Type: Conference or Workshop Item (Paper)
• Form of publication: Publisher's Version
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems
• Language: English