Image segmentation using wavelet-domain classification

Hyeokho Choi, Richard Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Scopus citations

Abstract

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

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSociety of Photo-Optical Instrumentation Engineers
Pages306-320
Number of pages15
Volume3816
StatePublished - 1999
EventProceedings of the 1999 Mathematical Modeling, Bayesian Estimation, and Inverse Problems - Denver, CO, USA
Duration: Jul 21 1999Jul 23 1999

Other

OtherProceedings of the 1999 Mathematical Modeling, Bayesian Estimation, and Inverse Problems
CityDenver, CO, USA
Period7/21/997/23/99

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

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