Boosting mixtures of Gaussians under normalized linear transformations for image classification

Ahmed Fawzi Otoom*, Oscar Perez Concha, Massimo Piccardi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

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%.

Original languageEnglish
Title of host publicationICITA 2011
Subtitle of host publication7th International Conference on Information Technology and Application
Place of PublicationNew York
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages184-189
Number of pages6
ISBN (Print)9780980326741
Publication statusPublished - 2011
Externally publishedYes
Event7th International Conference on Information Technology and Application, ICITA 2011 - Sydney, NSW, Australia
Duration: 21 Nov 201124 Nov 2011

Other

Other7th International Conference on Information Technology and Application, ICITA 2011
CountryAustralia
CitySydney, NSW
Period21/11/1124/11/11

Keywords

  • AdaBoost
  • Dimensionality reduction
  • Image classification
  • Linear transformation

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