Image segmentation using hidden Markov Gauss mixture models

Kyungsuk Pyun, Johan Lim, Chee Sun Won, Robert M. Gray

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.

Original languageEnglish
Pages (from-to)1902-1911
Number of pages10
JournalIEEE Transactions on Image Processing
Volume16
Issue number7
DOIs
StatePublished - Jul 2007

Keywords

  • 2-D hidden Markov models (HMMs)
  • Bond-percolation (BP) model
  • Gauss mixture models (GMMs)
  • Gauss mixture vector quantizer (GMVQ)
  • Image classification
  • Image segmentation
  • Parameter estimation

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