Robust cross-media transfer for visual event detection

Yang, Y; Huang, Z; Liu, J; Ma, Z

Robust cross-media transfer for visual event detection

Yang, Y

Huang, Z Liu, J Ma, Z

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Publication Type:: Conference Proceeding
Citation:: MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia, 2012, pp. 1045 - 1048
Issue Date:: 2012-12-26

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Field	Value	Language
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Huang, Z	en_US
dc.contributor.author	Liu, J	en_US
dc.contributor.author	Ma, Z	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. 1045 - 1048	en_US
dc.identifier.isbn	9781450310895	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121210
dc.description.abstract	In this paper, we present a novel approach, named Robust Cross-Media Transfer (RCMT), for visual event detection in social multimedia environments. Different from most existing methods, the proposed method can directly take different types of noisy social multimedia data as input and conduct robust event detection. More specifically, we build a robust model by employing an l2,1-norm regression model featuring noise tolerance, and also manage to integrate different types of social multimedia data by minimizing the distribution difference among them. Experimental results on real-life Flickr image dataset and YouTube video dataset demonstrate the effectiveness of our proposal, compared to state-of-the-art algorithms. © 2012 ACM.	en_US
dc.relation.ispartof	MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia	en_US
dc.relation.isbasedon	10.1145/2393347.2396379	en_US
dc.title	Robust cross-media transfer for visual event detection	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:

In this paper, we present a novel approach, named Robust Cross-Media Transfer (RCMT), for visual event detection in social multimedia environments. Different from most existing methods, the proposed method can directly take different types of noisy social multimedia data as input and conduct robust event detection. More specifically, we build a robust model by employing an l2,1-norm regression model featuring noise tolerance, and also manage to integrate different types of social multimedia data by minimizing the distribution difference among them. Experimental results on real-life Flickr image dataset and YouTube video dataset demonstrate the effectiveness of our proposal, compared to state-of-the-art algorithms. © 2012 ACM.

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http://hdl.handle.net/10453/121210