Texture classification based on multiple Gauss mixture vector quantizers

Kyungsuk Pyun, Chee Sun Won, Johan Lim, Robert M. Gray

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

17 Scopus citations

Abstract

We propose a texture classification method using multiple Gauss mixture vector quantizers (GMVQ). We designed a separate model codebook or Gauss mixture for each texture using the generalized Lloyd algorithm with a minimum discrimination information (MDI) distortion based on a training data set. The multi-codebook structure of the GMVQ classifier is an extension to images of the isolated utterance speech recognizer of J.E. Shore and D. Burton (see Proc. Int. Conf. Acoust., Speech, and Sig. Processing, IEEE82Ch.1746-7, p.907-10, 1982). We applied the algorithm to the Brodatz texture database and showed it to be competitive in performance in comparison to other texture classifiers. Its low complexity implementation and real-time operation make the approach suitable for content-based image retrieval.

Original languageEnglish
Title of host publicationProceedings - 2002 IEEE International Conference on Multimedia and Expo, ICME 2002
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages501-504
Number of pages4
ISBN (Electronic)0780373049
DOIs
StatePublished - 2002
Event2002 IEEE International Conference on Multimedia and Expo, ICME 2002 - Lausanne, Switzerland
Duration: 26 Aug 200229 Aug 2002

Publication series

NameProceedings - 2002 IEEE International Conference on Multimedia and Expo, ICME 2002
Volume2

Conference

Conference2002 IEEE International Conference on Multimedia and Expo, ICME 2002
Country/TerritorySwitzerland
CityLausanne
Period26/08/0229/08/02

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