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Texture classification based on multiple Gauss mixture vector quantizers

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4 Author(s)
Kyungsuk Pyun ; Dept. of Electr. Eng., Stanford Univ., CA, USA ; Chee Sun Won ; Johan Lim ; Gray, R.M.

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.

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Multimedia and Expo, 2002. ICME '02. Proceedings. 2002 IEEE International Conference on  (Volume:2 )

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