Minimum-risk temporal alignment of videos

Wang, Z; Piccardi, M

Minimum-risk temporal alignment of videos

Wang, Z Piccardi, M

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Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2018, 77 (12), pp. 14891 - 14906
Issue Date:: 2018-06-01

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Field	Value	Language
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.available	2020-05-25T19:13:59Z
dc.date.issued	2018-06-01	en_US
dc.identifier.citation	Multimedia Tools and Applications, 2018, 77 (12), pp. 14891 - 14906	en_US
dc.identifier.issn	1380-7501	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117687
dc.description.abstract	© 2017, Springer Science+Business Media, LLC. Temporal alignment of videos is an important requirement of tasks such as video comparison, analysis and classification. Most of the approaches proposed to date for video alignment leverage dynamic programming algorithms whose parameters are manually tuned. Conversely, this paper proposes a model that can learn its parameters automatically by minimizing a meaningful loss function over a given training set of videos and alignments. For learning, we exploit the effective framework of structural SVM and we extend it with an original scoring function that suitably scores the alignment of two given videos, and a loss function that quantifies the accuracy of a predicted alignment. The experimental results from four video action datasets show that the proposed model has been able to outperform a baseline and a state-of-the-art algorithm by a large margin in terms of alignment accuracy.	en_US
dc.relation.ispartof	Multimedia Tools and Applications	en_US
dc.relation.isbasedon	10.1007/s11042-017-5073-3	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Minimum-risk temporal alignment of videos	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	77	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	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.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	77	en_US

Abstract:

© 2017, Springer Science+Business Media, LLC. Temporal alignment of videos is an important requirement of tasks such as video comparison, analysis and classification. Most of the approaches proposed to date for video alignment leverage dynamic programming algorithms whose parameters are manually tuned. Conversely, this paper proposes a model that can learn its parameters automatically by minimizing a meaningful loss function over a given training set of videos and alignments. For learning, we exploit the effective framework of structural SVM and we extend it with an original scoring function that suitably scores the alignment of two given videos, and a loss function that quantifies the accuracy of a predicted alignment. The experimental results from four video action datasets show that the proposed model has been able to outperform a baseline and a state-of-the-art algorithm by a large margin in terms of alignment accuracy.

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http://hdl.handle.net/10453/117687