Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 2927-2937 |
| Seitenumfang | 11 |
| ISBN (elektronisch) | 9780738142661 |
| Publikationsstatus | Veröffentlicht - 2021 |
| Veranstaltung | 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 - Virtual, Online, USA / Vereinigte Staaten Dauer: 5 Jan. 2021 → 9 Jan. 2021 |
Abstract
Event classification can add valuable information for semantic search and the increasingly important topic of fact validation in news. So far, only few approaches address image classification for newsworthy event types such as natural disasters, sports events, or elections. Previous work distinguishes only between a limited number of event types and relies on rather small datasets for training. In this paper, we present a novel ontology-driven approach for the classification of event types in images. We leverage a large number of real-world news events to pursue two objectives: First, we create an ontology based on Wikidata comprising the majority of event types. Second, we introduce a novel large-scale dataset that was acquired through Web crawling. Several baselines are proposed including an ontology-driven learning approach that aims to exploit structured information of a knowledge graph to learn relevant event relations using deep neural networks. Experimental results on existing as well as novel benchmark datasets demonstrate the superiority of the proposed ontology-driven approach.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Angewandte Informatik
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Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021. Institute of Electrical and Electronics Engineers Inc., 2021. S. 2927-2937.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Ontology-driven event type classification in images
AU - Muller-Budack, Eric
AU - Springstein, Matthias
AU - Hakimov, Sherzod
AU - Mrutzek, Kevin
AU - Ewerth, Ralph
N1 - Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no 812997 (project name: ”CLEOPATRA”), and the German Research Foundation (DFG: Deutsche Forschungsgemein-schaft) under ”VIVA - Visual Information Search in Video Archives” (project number: 388420599).
PY - 2021
Y1 - 2021
N2 - Event classification can add valuable information for semantic search and the increasingly important topic of fact validation in news. So far, only few approaches address image classification for newsworthy event types such as natural disasters, sports events, or elections. Previous work distinguishes only between a limited number of event types and relies on rather small datasets for training. In this paper, we present a novel ontology-driven approach for the classification of event types in images. We leverage a large number of real-world news events to pursue two objectives: First, we create an ontology based on Wikidata comprising the majority of event types. Second, we introduce a novel large-scale dataset that was acquired through Web crawling. Several baselines are proposed including an ontology-driven learning approach that aims to exploit structured information of a knowledge graph to learn relevant event relations using deep neural networks. Experimental results on existing as well as novel benchmark datasets demonstrate the superiority of the proposed ontology-driven approach.
AB - Event classification can add valuable information for semantic search and the increasingly important topic of fact validation in news. So far, only few approaches address image classification for newsworthy event types such as natural disasters, sports events, or elections. Previous work distinguishes only between a limited number of event types and relies on rather small datasets for training. In this paper, we present a novel ontology-driven approach for the classification of event types in images. We leverage a large number of real-world news events to pursue two objectives: First, we create an ontology based on Wikidata comprising the majority of event types. Second, we introduce a novel large-scale dataset that was acquired through Web crawling. Several baselines are proposed including an ontology-driven learning approach that aims to exploit structured information of a knowledge graph to learn relevant event relations using deep neural networks. Experimental results on existing as well as novel benchmark datasets demonstrate the superiority of the proposed ontology-driven approach.
UR - http://www.scopus.com/inward/record.url?scp=85116121270&partnerID=8YFLogxK
U2 - 10.1109/WACV48630.2021.00297
DO - 10.1109/WACV48630.2021.00297
M3 - Conference contribution
AN - SCOPUS:85116121270
SP - 2927
EP - 2937
BT - Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
Y2 - 5 January 2021 through 9 January 2021
ER -