Abstract: We propose a texture classification method using multiple Gauss mixture vector quantizers (GMVQ). We designed a separate model codebook or Gauss ...
We propose a texture classification method using multiple. Gauss mixture vector quantizers. We designed a separate model codebook or Gauss mixture for each ...
A separate model codebook or Gauss mixture for each texture is designed using the generalized Lloyd algorithm with a minimum discrimination information ...
Kyungsuk Pyun, Chee Sun Won , Johan Lim, Robert M. Gray: Texture classification based on multiple Gauss mixture vector quantizers. ICME (2) 2002: 501-504.
For texture classification, the observation probability distribution for each texture can be estimated using a Gauss mixture vector quantizer (GMVQ) ...
Abstract. Gauss mixture (GM) models are frequently used for their ability to well approximate many densities and for their tractability to analysis.
Gauss mixtures are a popular class of models in statistics and sta- tistical signal processing because Gauss mixtures can provide good fits to smooth densities, ...
The resulting Lloyd clustering algorithm is demonstrated by applications to image vector quantization, texture classification, and North Atlantic pipeline image ...
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공동 저자 ; Texture classification based on multiple Gauss mixture vector quantizers. K Pyun, CS Won, J Lim, RM Gray. Proceedings. IEEE International Conference ...
The generalized Gaussian mixture model includes several lepto kurtic, platy kurtic and meso kurtic distributions as particular cases. The model parameters are ...