Texture analysis with variational hidden Markov trees

Nilanjan Dasgupta, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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. © 2006 IEEE.
Original languageEnglish (US)
Pages (from-to)2352-2356
Number of pages5
JournalIEEE Transactions on Signal Processing
Issue number6 I
StatePublished - Jun 1 2006
Externally publishedYes


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