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A New Approach to Unsupervised Image Segmentation Based on Wavelet-Domain Hidden Markov Tree Models

  • Qiang Sun
  • Shuiping Gou
  • Licheng Jiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

In this paper, a new unsupervised image segmentation scheme is presented, which combines wavelet-domain hidden Markov tree (HMT) model and possibilistic C-means (PCM) clustering algorithm. As an efficient soft clustering algorithm, PCM is introduce into unsupervised image segmentation and used to cluster model likelihoods for different image blocks to identify corresponding image samples, on the basis of which the unsupervised segmentation problem is converted into a self-supervised segmentation one. The simulation results on synthetic mosaics, aerial photo and synthetic aperture radar (SAR) image show that the new unsupervised image segmentation technique can obtain much better image segmentation performance than the approach based on K-means clustering.

Keywords

Image Segmentation Gaussian Mixture Model Wavelet Coefficient Synthetic Aperture Radar Synthetic Aperture Radar Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Qiang Sun
    • 1
  • Shuiping Gou
    • 1
  • Licheng Jiao
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’anChina

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