Details
| Original language | English |
|---|---|
| Title of host publication | MM 2021 |
| Subtitle of host publication | Proceedings of the 29th ACM International Conference on Multimedia |
| Pages | 2801-2803 |
| Number of pages | 3 |
| ISBN (electronic) | 9781450386517 |
| Publication status | Published - 17 Oct 2021 |
| Event | 29th ACM International Conference on Multimedia, MM 2021 - Virtual, Online, China Duration: 20 Oct 2021 → 24 Oct 2021 |
Publication series
| Name | MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia |
|---|
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.
Keywords
- art retrieval, cross-modal retrieval, deep learning, web application
ASJC Scopus subject areas
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
Cite this
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MM 2021 : Proceedings of the 29th ACM International Conference on Multimedia. 2021. p. 2801-2803 (MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - iART
T2 - 29th ACM International Conference on Multimedia, MM 2021
AU - Springstein, Matthias
AU - Schneider, Stefanie
AU - Rahnama, Javad
AU - Hüllermeier, Eyke
AU - Kohle, Hubertus
AU - Ewerth, Ralph
PY - 2021/10/17
Y1 - 2021/10/17
N2 - 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.
AB - 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.
KW - art retrieval
KW - cross-modal retrieval
KW - deep learning
KW - web application
UR - http://www.scopus.com/inward/record.url?scp=85119346617&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2108.01542
DO - 10.48550/arXiv.2108.01542
M3 - Conference contribution
AN - SCOPUS:85119346617
T3 - MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia
SP - 2801
EP - 2803
BT - MM 2021
Y2 - 20 October 2021 through 24 October 2021
ER -