Minimum-Risk Structured Learning of Video Summarization

Hussein, F; Piccardi, M

Minimum-Risk Structured Learning of Video Summarization

Hussein, F Piccardi, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017, 2017, 2017-January pp. 248 - 251
Issue Date:: 2017-12-28

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Field	Value	Language
dc.contributor.author	Hussein, F	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2017-12-28	en_US
dc.identifier.citation	Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017, 2017, 2017-January pp. 248 - 251	en_US
dc.identifier.isbn	9781538629369	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121831
dc.description.abstract	© 2017 IEEE. Video summarization is an important multimedia task for applications such as video indexing and retrieval, video surveillance, human-computer interaction and video 'storyboarding'. In this paper, we present a new approach for automatic summarization of video collections that leverages a structured minimum-risk classifier and efficient submodular inference. To test the accuracy of the predicted summaries we utilize a recently-proposed measure (V-JAUNE) that considers both the content and frame order of the original video. Qualitative and quantitative tests over two action video datasets - the ACE and the MSR DailyActivity3D datasets - show that the proposed approach delivers more accurate summaries than the compared minimum-risk and syntactic approaches.	en_US
dc.relation.ispartof	Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017	en_US
dc.relation.isbasedon	10.1109/ISM.2017.41	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Minimum-Risk Structured Learning of Video Summarization	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-January	en_US
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2017-January	en_US

Abstract:

© 2017 IEEE. Video summarization is an important multimedia task for applications such as video indexing and retrieval, video surveillance, human-computer interaction and video 'storyboarding'. In this paper, we present a new approach for automatic summarization of video collections that leverages a structured minimum-risk classifier and efficient submodular inference. To test the accuracy of the predicted summaries we utilize a recently-proposed measure (V-JAUNE) that considers both the content and frame order of the original video. Qualitative and quantitative tests over two action video datasets - the ACE and the MSR DailyActivity3D datasets - show that the proposed approach delivers more accurate summaries than the compared minimum-risk and syntactic approaches.

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http://hdl.handle.net/10453/121831