Image classification based on a multiresolution two dimensional hidden Markov model

Jia Li, Robert M. Gray

Research output: Contribution to conferencePaperpeer-review

7 Scopus citations


This paper presents an image classification algorithm based upon a two dimensional multiresolution hidden Markov model (MHMM). This model represents an image by feature vectors in several resolutions and considers the feature vectors statistically dependent through an underlying state process assumed to be a multiscale Markov mesh. To estimate the model by the maximum likelihood criterion, approximations are made successively based on the EM algorithm to reach feasible computation. To classify an image, the algorithm attempts to find the optimal set of states with the maximum a posteriori probability. The states are then mapped into classes. The multiresolution model enables multiscale context information to be incorporated into classification. Suboptimal algorithms based on the model provide progressive classification which greatly speeds up classification based on single resolution HMMs.

Original languageEnglish (US)
Number of pages5
StatePublished - 1999
EventInternational Conference on Image Processing (ICIP'99) - Kobe, Jpn
Duration: Oct 24 1999Oct 28 1999


OtherInternational Conference on Image Processing (ICIP'99)
CityKobe, Jpn

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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