On gauss mixture vector quantizers and gabor wavelet classifiers for texture classification

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

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

Abstract

For texture classification, the observation probability distribution for each texture can be estimated using a Gauss mixture vector quantizer (GMVQ) designed with the generalized Lloyd algorithm with a minimum discrimination information (MDI) distortion. The designed multiple GMVQs are applied to classifying Brodatz textures. For low complexity implementation, Super-blocks are used to capture the macro features of the texture. In [1], the results were compared well to TSWT [2]. As an extension of [1], this paper shows that our multi-codebook GMVQ classifier, applied to the Brodatz texture database, outperforms state-of-the-art texture classifier, Gabor wavelet classifier [3].

Original languageEnglish
Title of host publicationConference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers
Pages1222-1225
Number of pages4
StatePublished - 2005
Event39th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: 28 Oct 20051 Nov 2005

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2005
ISSN (Print)1058-6393

Conference

Conference39th Asilomar Conference on Signals, Systems and Computers
Country/TerritoryUnited States
CityPacific Grove, CA
Period28/10/051/11/05

Fingerprint

Dive into the research topics of 'On gauss mixture vector quantizers and gabor wavelet classifiers for texture classification'. Together they form a unique fingerprint.

Cite this