A fully unsupervised image segmentation algorithm based on wavelet-domain hidden markov tree models

  • Authors:

  • Qiang Sun;Yuheng Sha;Xinbo Gao;Biao Hou;Licheng Jiao

  • Affiliations:

  • Institute of Intelligent Information Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing, Xidian University, Xi’an, China

  • Venue:
  • ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
  • Year:
  • 2005

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Abstract

A fully unsupervised image segmentation algorithm is presented in this paper, in which wavelet-domain hidden Markov tree (WD-HMT) model is exploited together with the cluster analysis and validity techniques. The true number of textures in a given image is determined by calculating the likelihood disparity of textures using the modified partition fuzzy degree (MPFD) function at one suitable scale. Then, possibilistic C-means (PCM) clustering is performed to determine the training sample data from different textures according to the true number of textures obtained. The unsupervised segmentation is changed into self-supervised one, and the HMTseg algorithm is used to achieve the final segmentation results. This algorithm is applied to segment a variety of composite texture images into distinct homogeneous regions and good segmentation results are reported.