Multimodal Classification of Urban Micro-Events
| Autor: | Stevan Rudinac, Marcel Worring, Maarten Sukel |
|---|---|
| Přispěvatelé: | Operations Management (ABS, FEB), Faculty of Science, Intelligent Sensory Information Systems (IVI, FNWI) |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning business.industry Computer science Machine Learning (stat.ML) 02 engineering and technology Machine learning computer.software_genre Machine Learning (cs.LG) Statistics - Machine Learning 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) Feature learning computer |
| Zdroj: | ACM Multimedia MM'19: proceedings of the 27th ACM Conference on Multimedia : October 21-25, 2019, Nice, France, 1455-1463 STARTPAGE=1455;ENDPAGE=1463;TITLE=MM'19 |
| Popis: | In this paper we seek methods to effectively detect urban micro- events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these events are difficult to identify in most data sources. However, by using citizen sensing to gather data, detecting them becomes feasible. The data gathered by citizen sensing is often multimodal and, as a consequence, the in- formation required to detect urban micro-events is distributed over multiple modalities. This makes it essential to have a classifier ca- pable of combining them. In this paper we explore several methods of creating such a classifier, including early, late and hybrid fusion as well as representation learning using multimodal graphs. We evaluate performance in terms of accurate classification of urban micro-events on a real world dataset obtained from a live citizen re- porting system. We show that a multimodal approach yields higher performance than unimodal alternatives. Furthermore, we demon- strate that our hybrid combination of early and late fusion with multimodal embeddings outperforms our other fusion methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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