Knowledge adaptation for ad hoc multimedia event detection with few exemplars

Ma, Z; Yang, Y; Cai, Y; Sebe, N; Hauptmann, AG

Knowledge adaptation for ad hoc multimedia event detection with few exemplars

Ma, Z Yang, Y

Cai, Y Sebe, N Hauptmann, AG

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Publication Type:: Conference Proceeding
Citation:: MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia, 2012, pp. 469 - 478
Issue Date:: 2012-12-26

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Field	Value	Language
dc.contributor.author	Ma, Z	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Cai, Y	en_US
dc.contributor.author	Sebe, N	en_US
dc.contributor.author	Hauptmann, AG	en_US
dc.date.issued	2012-12-26	en_US
dc.identifier.citation	MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia, 2012, pp. 469 - 478	en_US
dc.identifier.isbn	9781450310895	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121227
dc.description.abstract	Multimedia event detection (MED) has a significant impact on many applications. Though video concept annotation has received much research effort, video event detection remains largely unaddressed. Current research mainly focuses on sports and news event detection or abnormality detection in surveillance videos. Our research on this topic is capable of detecting more complicated and generic events. Moreover, the curse of reality, i.e., precisely labeled multimedia content is scarce, necessitates the study on how to attain respectable detection performance using only limited positive examples. Research addressing these two aforementioned issues is still in its infancy. In light of this, we explore Ad Hoc MED, which aims to detect complicated and generic events by using few positive examples. To the best of our knowledge, our work makes the first attempt on this topic. As the information from these few positive examples is limited, we propose to infer knowledge from other multimedia resources to facilitate event detection. Experiments are performed on real-world multimedia archives consisting of several challenging events. The results show that our approach outperforms several other detection algorithms. Most notably, our algorithm outperforms SVM by 43% and 14% comparatively in Average Precision when using Gaussian and χ2 kernel respectively. © 2012 ACM.	en_US
dc.relation.ispartof	MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia	en_US
dc.relation.isbasedon	10.1145/2393347.2393414	en_US
dc.title	Knowledge adaptation for ad hoc multimedia event detection with few exemplars	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Multimedia event detection (MED) has a significant impact on many applications. Though video concept annotation has received much research effort, video event detection remains largely unaddressed. Current research mainly focuses on sports and news event detection or abnormality detection in surveillance videos. Our research on this topic is capable of detecting more complicated and generic events. Moreover, the curse of reality, i.e., precisely labeled multimedia content is scarce, necessitates the study on how to attain respectable detection performance using only limited positive examples. Research addressing these two aforementioned issues is still in its infancy. In light of this, we explore Ad Hoc MED, which aims to detect complicated and generic events by using few positive examples. To the best of our knowledge, our work makes the first attempt on this topic. As the information from these few positive examples is limited, we propose to infer knowledge from other multimedia resources to facilitate event detection. Experiments are performed on real-world multimedia archives consisting of several challenging events. The results show that our approach outperforms several other detection algorithms. Most notably, our algorithm outperforms SVM by 43% and 14% comparatively in Average Precision when using Gaussian and χ2 kernel respectively. © 2012 ACM.

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http://hdl.handle.net/10453/121227