Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | MediaEval 2018 Multimedia Benchmark Workshop |
| Untertitel | Working Notes Proceedings of the MediaEval 2018 Workshop |
| Publikationsstatus | Veröffentlicht - 2018 |
| Veranstaltung | MediaEval Workshop, MediaEval 2018 - Sophia Antipolis, Frankreich Dauer: 29 Okt. 2018 → 31 Okt. 2018 |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 2283 |
| ISSN (Print) | 1613-0073 |
Abstract
Social media, which provides instant textual and visual information exchange, plays a more important role in emergency response than ever before. Many researchers nowadays are focusing on disaster monitoring using crowd sourcing. Interpretation and retrieval of such information significantly influences the efficiency of these applications. This paper presents a method proposed by team EVUS-ikg for the MediaEval 2018 challenge on Multimedia Satellite Task. We only focused on the subtask "flood classification for social multimedia". A supervised learning method with an ensemble of 10 Convolutional Neural Networks (CNN) was applied to classify the tweets in the benchmark. Copyright held by the owner/author(s).
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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MediaEval 2018 Multimedia Benchmark Workshop: Working Notes Proceedings of the MediaEval 2018 Workshop. 2018. (CEUR Workshop Proceedings; Band 2283).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Ensembled Convolutional Neural Network Models for Retrieving Flood Relevant Tweets
AU - Feng, Yu
AU - Shebotnov, Sergiy
AU - Brenner, Claus
AU - Sester, Monika
PY - 2018
Y1 - 2018
N2 - Social media, which provides instant textual and visual information exchange, plays a more important role in emergency response than ever before. Many researchers nowadays are focusing on disaster monitoring using crowd sourcing. Interpretation and retrieval of such information significantly influences the efficiency of these applications. This paper presents a method proposed by team EVUS-ikg for the MediaEval 2018 challenge on Multimedia Satellite Task. We only focused on the subtask "flood classification for social multimedia". A supervised learning method with an ensemble of 10 Convolutional Neural Networks (CNN) was applied to classify the tweets in the benchmark. Copyright held by the owner/author(s).
AB - Social media, which provides instant textual and visual information exchange, plays a more important role in emergency response than ever before. Many researchers nowadays are focusing on disaster monitoring using crowd sourcing. Interpretation and retrieval of such information significantly influences the efficiency of these applications. This paper presents a method proposed by team EVUS-ikg for the MediaEval 2018 challenge on Multimedia Satellite Task. We only focused on the subtask "flood classification for social multimedia". A supervised learning method with an ensemble of 10 Convolutional Neural Networks (CNN) was applied to classify the tweets in the benchmark. Copyright held by the owner/author(s).
UR - http://www.scopus.com/inward/record.url?scp=85059857711&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85059857711
T3 - CEUR Workshop Proceedings
BT - MediaEval 2018 Multimedia Benchmark Workshop
T2 - MediaEval Workshop, MediaEval 2018
Y2 - 29 October 2018 through 31 October 2018
ER -