Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | MM 2021 |
| Untertitel | Proceedings of the 29th ACM International Conference on Multimedia |
| Seiten | 2801-2803 |
| Seitenumfang | 3 |
| ISBN (elektronisch) | 9781450386517 |
| Publikationsstatus | Veröffentlicht - 17 Okt. 2021 |
| Veranstaltung | 29th ACM International Conference on Multimedia, MM 2021 - Virtual, Online, China Dauer: 20 Okt. 2021 → 24 Okt. 2021 |
Publikationsreihe
| 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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Software
- Informatik (insg.)
- Computergrafik und computergestütztes Design
Zitieren
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- BibTex
- RIS
MM 2021 : Proceedings of the 29th ACM International Conference on Multimedia. 2021. S. 2801-2803 (MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › 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 -