The infinite hidden Markov random field model

IEEE Transactions on Neural Networks

Volumn 21, Issue 6, 2010, Pages 1004-1014

  Chatzis, Sotirios P ^a   Tsechpenakis, Gabriel ^a  

^a UNIVERSITY OF MIAMI   (United States)

Author keywords

Bayesian inference; FDirichlet process; Hidden Markov random field; Nonparametric models

Indexed keywords

APRIORI; AUTOMATIC SELECTION; BAYESIAN INFERENCE; CLUSTERING APPLICATIONS; COMPUTATIONAL REQUIREMENTS; CONSTRAINED CLUSTERING; DIRICHLET PROCESS; DIRICHLET PROCESS MIXTURE; EXISTING METHOD; HIDDEN MARKOV RANDOM FIELD MODEL; HIDDEN MARKOV RANDOM FIELDS; MARKOV CHAIN MONTE CARLO METHOD; MARKOV RANDOM FIELD; MIXTURE MODEL; MODEL SIZE; NON-PARAMETRIC BAYESIAN; NON-PARAMETRIC MODEL; NUMBER OF CLUSTERS; REGION CLUSTER; UNSUPERVISED IMAGE SEGMENTATION; VARIATIONAL BAYESIAN;

BAYESIAN NETWORKS; DIGITAL IMAGE STORAGE; HIDDEN MARKOV MODELS; INFERENCE ENGINES; MIXTURES; MODELS; MONTE CARLO METHODS;

IMAGE SEGMENTATION;

ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; INFORMATION RETRIEVAL; METHODOLOGY; PROBABILITY; STATISTICAL MODEL;

ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTER SIMULATION; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MARKOV CHAINS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 77953596396     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2046910     Document Type: Article

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