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Hierarchical multiple Markov chain model for unsupervised texture segmentation.

Giuseppe Scarpa1, Raffaele Gaetano, Michal Haindl

  • 1University "Federico II", DIBET, 80125, Naples, Italy. giscarpa@unina.it

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 19, 2009
PubMed
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This study introduces a new multiscale texture model (Hierarchical Multiple Markov Chain) and algorithm for unsupervised image segmentation. The method effectively segments color images by analyzing texture interactions across multiple scales.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Unsupervised image segmentation remains a challenge, particularly for complex natural textures.
  • Existing methods often struggle to capture multiscale texture information effectively.

Purpose of the Study:

  • To introduce a novel multiscale texture model, the Hierarchical Multiple Markov Chain (H-MMC).
  • To develop an unsupervised algorithm (Texture Fragmentation and Reconstruction - TFR) for color image segmentation based on the H-MMC model.

Main Methods:

  • Characterizing elementary textures via spatial interactions modeled by Markov chains.
  • Developing feature vectors to describe textures and recursively merging them into larger, multiscale textures.
  • Implementing the TFR algorithm with fragmentation and reconstruction steps using a probabilistic texture score.

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Main Results:

  • The H-MMC model provides a hierarchical representation of textures at different observation scales.
  • The TFR algorithm successfully performs unsupervised image segmentation.
  • Evaluated performance on the Prague segmentation benchmark and real-world natural and remote sensing images.

Conclusions:

  • The proposed H-MMC model and TFR algorithm offer a robust approach to unsupervised multiscale image segmentation.
  • The method demonstrates effectiveness on diverse image datasets, including natural and remote sensing imagery.