Multiscale image segmentation using wavelet-domain hidden Markov models

Hyeokho Choi, Richard G. Baraniuk

Research output: Contribution to journalArticlepeer-review

338 Scopus citations


We introduce a new image texture segmentation algorithm, HMTseg, based on wavelets and the hidden Markov tree (HMT) model. The HMT is a tree-structured probabilistic graph that captures the statistical properties of the coefficients of the wavelet transform. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides a good classifier for distinguishing between textures. Utilizing the inherent tree structure of the wavelet HMT and its fast training and likelihood computation algorithms, we perform texture classification at a range of different scales. We then fuse these multiscale classifications using a Bayesian probabilistic graph to obtain reliable final segmentations. Since HMTseg works on the wavelet transform of the image, it can directly segment wavelet-compressed images without the need for decompression into the space domain. We demonstrate the performance of HMTseg with synthetic, aerial photo, and document image segmentations.

Original languageEnglish (US)
Pages (from-to)1309-1321
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number9
StatePublished - Sep 2001


  • Hidden Markov tree
  • Segmentation
  • Texture modeling
  • Wavelets

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Graphics and Computer-Aided Design
  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition


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