ORCID: https://orcid.org/0000-0003-4915-6949; Rahnama, Javad; Hüllermeier, Eyke
ORCID: https://orcid.org/0000-0002-9944-4108; Kohle, Hubertus
ORCID: https://orcid.org/0000-0003-3162-1304 und Ewerth, Ralph
ORCID: https://orcid.org/0000-0003-0918-6297
(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
[PDF, 14MB]

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) |
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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 |
URN: | urn:nbn:de:bvb:19-epub-92516-9 |
Language: | English |
Item ID: | 92516 |
Date Deposited: | 09. Aug 2022 18:10 |
Last Modified: | 27. Nov 2024 15:53 |