Boosting mixtures of gaussians under normalized linear transformations for image classification

Otoom, AF; Concha, OP; Piccardi, M

Boosting mixtures of gaussians under normalized linear transformations for image classification

Otoom, AF Concha, OP Piccardi, M

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Publication Type:: Conference Proceeding
Citation:: 7th International Conference on Information Technology and Application, ICITA 2011, 2011, pp. 184 - 189
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Otoom, AF	en_US
dc.contributor.author	Concha, OP	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	7th International Conference on Information Technology and Application, ICITA 2011, 2011, pp. 184 - 189	en_US
dc.identifier.isbn	9780980326741	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31332
dc.description.abstract	We address the problem of image classification. Our aim is to improve the performance of MLiT: mixture of Gaussians under Linear transformations, a feature-based classifier proposed in [1] aiming to reduce dimensionality based on a linear transformation which is not restricted to be orthogonal. Boosting might offer an interesting solution by improving the performance of a given base classification algorithm. In this paper, we propose to integrate MLiT within the framework of AdaBoost, which is a widely applied method for boosting. For experimental validation, we have evaluated the proposed method on the four UCI data sets (Vehicle, OpticDigit, WDBC, WPBC) [2] and the author's own. Boosting has proved capable of enhancing the performance of the base classifier on two data sets with improvements of up to 12.8%.	en_US
dc.relation.ispartof	7th International Conference on Information Technology and Application, ICITA 2011	en_US
dc.title	Boosting mixtures of gaussians under normalized linear transformations for image classification	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney Australia
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic 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:

We address the problem of image classification. Our aim is to improve the performance of MLiT: mixture of Gaussians under Linear transformations, a feature-based classifier proposed in [1] aiming to reduce dimensionality based on a linear transformation which is not restricted to be orthogonal. Boosting might offer an interesting solution by improving the performance of a given base classification algorithm. In this paper, we propose to integrate MLiT within the framework of AdaBoost, which is a widely applied method for boosting. For experimental validation, we have evaluated the proposed method on the four UCI data sets (Vehicle, OpticDigit, WDBC, WPBC) [2] and the author's own. Boosting has proved capable of enhancing the performance of the base classifier on two data sets with improvements of up to 12.8%.

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http://hdl.handle.net/10453/31332