DocumentCode
945541
Title
Texture analysis with variational hidden Markov trees
Author
Dasgupta, Nilanjan ; Carin, Lawrence
Author_Institution
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
Volume
54
Issue
6
fYear
2006
fDate
6/1/2006 12:00:00 AM
Firstpage
2353
Lastpage
2356
Abstract
A variational Bayes formulation of the hidden Markov tree (HMT) model is proposed for texture analysis, utilizing a multilevel wavelet decomposition of imagery. The variational method yields an approximation to the full posterior of the HMT parameters. Texture classification is based on the posterior predictive distribution or marginalized evidence, with example results presented.
Keywords
Bayes methods; hidden Markov models; image classification; image texture; trees (mathematics); wavelet transforms; imagery multilevel wavelet decomposition; porterior predictive distribution; texture analysis; texture classification; variational Bayes formulation; variational hidden Markov trees; Hidden Markov models; Image analysis; Image classification; Image texture analysis; Maximum likelihood estimation; Statistical distributions; Training data; Two dimensional displays; Wavelet analysis; Wavelet coefficients; HMT; Kullback–Leibler divergence; texture classification; variational Bayes;
fLanguage
English
Journal_Title
Signal Processing, IEEE Transactions on
Publisher
ieee
ISSN
1053-587X
Type
jour
DOI
10.1109/TSP.2006.872588
Filename
1634828
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