Event detection using multi-level relevance labels and multiple features

Xu, Z; Tsang, IW; Yang, Y; Ma, Z; Hauptmann, AG

Event detection using multi-level relevance labels and multiple features

Xu, Z Tsang, IW

Yang, Y

Ma, Z Hauptmann, AG

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 97 - 104
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Xu, Z	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Ma, Z	en_US
dc.contributor.author	Hauptmann, AG	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 97 - 104	en_US
dc.identifier.isbn	9781479951178	en_US
dc.identifier.issn	1063-6919	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121618
dc.description.abstract	© 2014 IEEE. We address the challenging problem of utilizing related exemplars for complex event detection while multiple features are available. Related exemplars share certain positive elements of the event, but have no uniform pattern due to the huge variance of relevance levels among different related exemplars. None of the existing multiple feature fusion methods can deal with the related exemplars. In this paper, we propose an algorithm which adaptively utilizes the related exemplars by cross-feature learning. Ordinal labels are used to represent the multiple relevance levels of the related videos. Label candidates of related exemplars are generated by exploring the possible relevance levels of each related exemplar via a cross-feature voting strategy. Maximum margin criterion is then applied in our framework to discriminate the positive and negative exemplars, as well as the related exemplars from different relevance levels. We test our algorithm using the large scale TRECVID 2011 dataset and it gains promising performance.	en_US
dc.relation.ispartof	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/CVPR.2014.20	en_US
dc.title	Event detection using multi-level relevance labels and multiple features	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. We address the challenging problem of utilizing related exemplars for complex event detection while multiple features are available. Related exemplars share certain positive elements of the event, but have no uniform pattern due to the huge variance of relevance levels among different related exemplars. None of the existing multiple feature fusion methods can deal with the related exemplars. In this paper, we propose an algorithm which adaptively utilizes the related exemplars by cross-feature learning. Ordinal labels are used to represent the multiple relevance levels of the related videos. Label candidates of related exemplars are generated by exploring the possible relevance levels of each related exemplar via a cross-feature voting strategy. Maximum margin criterion is then applied in our framework to discriminate the positive and negative exemplars, as well as the related exemplars from different relevance levels. We test our algorithm using the large scale TRECVID 2011 dataset and it gains promising performance.

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http://hdl.handle.net/10453/121618