Active learning for human action recognition with Gaussian processes

Liu, X; Zhang, J

Active learning for human action recognition with Gaussian processes

Liu, X Zhang, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2011, pp. 3253 - 3256
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Image Processing, ICIP, 2011, pp. 3253 - 3256	en_US
dc.identifier.isbn	9781457713033	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22260
dc.description.abstract	This paper presents an active learning approach for recognizing human actions in videos based on multiple kernel combined method. We design the classifier based on Multiple Kernel Learning (MKL) through Gaussian Processes (GP) regression. This classifier is then trained in an active learning approach. In each iteration, one optimal sample is selected to be interactively annotated and incorporated into training set. The selection of the sample is based on the heuristic feedback of the GP classifier. To our knowledge, GP regression MKL based active learning methods have not been applied to address the human action recognition yet. We test this approach on standard benchmarks. This approach outperforms the state-of-the-art techniques in accuracy while requires significantly less training samples. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.relation.isbasedon	10.1109/ICIP.2011.6116363	en_US
dc.title	Active learning for human action recognition with Gaussian processes	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Brussels, Belgium	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents an active learning approach for recognizing human actions in videos based on multiple kernel combined method. We design the classifier based on Multiple Kernel Learning (MKL) through Gaussian Processes (GP) regression. This classifier is then trained in an active learning approach. In each iteration, one optimal sample is selected to be interactively annotated and incorporated into training set. The selection of the sample is based on the heuristic feedback of the GP classifier. To our knowledge, GP regression MKL based active learning methods have not been applied to address the human action recognition yet. We test this approach on standard benchmarks. This approach outperforms the state-of-the-art techniques in accuracy while requires significantly less training samples. © 2011 IEEE.

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http://hdl.handle.net/10453/22260