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Unsupervised texture segmentation using multichannel decomposition and hidden Markov models.

J L Chen1, A Kundu

  • 1Dept. of Comput. Sci., Chung-Hua Polytech. Inst., Hsinchu.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study introduces an automatic, unsupervised texture segmentation method using hidden Markov models (HMMs). The novel approach effectively identifies and segments distinct textures within images, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Texture segmentation is crucial for image analysis.
  • Existing methods often require supervision or lack robustness.
  • Hidden Markov Models (HMMs) offer a probabilistic framework for sequence modeling.

Purpose of the Study:

  • To develop an automatic and unsupervised texture segmentation scheme.
  • To leverage HMMs for modeling and discriminating between different textures.
  • To improve the accuracy and efficiency of texture segmentation.

Main Methods:

  • Image feature extraction using Laws' micromasks and directional macromasks.
  • Modeling texture regions as Hidden Markov Models (HMMs).
  • A two-stage segmentation (coarse and fine) with HMM merging based on discrimination information (DI).

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Main Results:

  • The proposed HMM-based scheme successfully segments images into distinct texture regions.
  • The two-stage approach accurately estimates the number and parameters of HMMs.
  • Postprocessing with multiscale majority filtering further enhances segmentation quality.

Conclusions:

  • The developed automatic unsupervised texture segmentation scheme using HMMs is effective.
  • The method demonstrates competitive performance compared to existing literature.
  • The scheme is suitable for efficient pipeline/parallel implementation.