Structural SVM with Partial Ranking for Activity Segmentation and Classification

Zhang, G; Piccardi, M

Structural SVM with Partial Ranking for Activity Segmentation and Classification

Zhang, G Piccardi, M

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Publication Type:: Journal Article
Citation:: IEEE Signal Processing Letters, 2015, 22 (12), pp. 2344 - 2348
Issue Date:: 2015-12-01

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Field	Value	Language
dc.contributor.author	Zhang, G	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2015-12-01	en_US
dc.identifier.citation	IEEE Signal Processing Letters, 2015, 22 (12), pp. 2344 - 2348	en_US
dc.identifier.issn	1070-9908	en_US
dc.identifier.uri	http://hdl.handle.net/10453/40982
dc.description.abstract	© 1994-2012 IEEE. Structural SVM is an extension of the support vector machine for the joint prediction of structured labels from multiple measurements. Following a large margin principle, the training of structural SVM ensures that the ground-Truth labeling of each sample receives a score higher than that of any other labeling. However, no specific score ranking is imposed among the other labelings. In this letter, we extend the standard constraint set of structural SVM with constraints between 'almost-correct' labelings and less desirable ones to obtain a partial-ranking structural SVM (PR-SSVM) approach. Experimental results on action segmentation and classification with two challenging datasets (the TUM Kitchen mocap dataset and the CMU-MMAC video dataset) show that the proposed method achieves better detection and false alarm rates and higher F1 scores than both the conventional structural SVM and a comparable unstructured predictor. The proposed method also achieves higher accuracy than the state of the art on these datasets in excess of 14 and 31 percentage points, respectively.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0987387
dc.relation.ispartof	IEEE Signal Processing Letters	en_US
dc.relation.isbasedon	10.1109/LSP.2015.2480097	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Structural SVM with Partial Ranking for Activity Segmentation and Classification	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	22	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	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	22	en_US

Abstract:

© 1994-2012 IEEE. Structural SVM is an extension of the support vector machine for the joint prediction of structured labels from multiple measurements. Following a large margin principle, the training of structural SVM ensures that the ground-Truth labeling of each sample receives a score higher than that of any other labeling. However, no specific score ranking is imposed among the other labelings. In this letter, we extend the standard constraint set of structural SVM with constraints between 'almost-correct' labelings and less desirable ones to obtain a partial-ranking structural SVM (PR-SSVM) approach. Experimental results on action segmentation and classification with two challenging datasets (the TUM Kitchen mocap dataset and the CMU-MMAC video dataset) show that the proposed method achieves better detection and false alarm rates and higher F1 scores than both the conventional structural SVM and a comparable unstructured predictor. The proposed method also achieves higher accuracy than the state of the art on these datasets in excess of 14 and 31 percentage points, respectively.

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http://hdl.handle.net/10453/40982