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Image segmentation and labeling using the Polya urn model.

A Banerjee1, P Burlina, F Alajaji

  • 1Center for Autom. Res., Maryland Univ., College Park, MD 20742-3275, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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This study introduces a novel image segmentation method using a modified Polya urn model to identify homogeneous regions. The approach mimics contagious spread for improved segmentation accuracy in various image types.

Area of Science:

  • Computer Vision
  • Image Processing
  • Statistical Modeling

Background:

  • Image segmentation is crucial for analyzing visual data.
  • Existing methods may struggle with complex textures and contagious phenomena.
  • Polya's urn model offers a probabilistic framework for modeling such processes.

Purpose of the Study:

  • To develop a new image segmentation technique inspired by contagious phenomena.
  • To generalize Polya's urn model for spatial interactions in image analysis.
  • To evaluate the proposed method's performance on diverse image datasets.

Main Methods:

  • A preliminary segmentation initializes pixel urn compositions.
  • A modified urn sampling scheme, simulating infection spread, segments images.

Related Experiment Videos

  • Contagion urn processes with spatial Markovian relationships update urn compositions iteratively.

    • Main Results:

    • The method successfully segments synthetic texture, UWB SAR, and MRI images.
    • Comparisons show competitive or improved performance against simulated annealing and relaxation labeling.
    • Asymptotic behavior of the contagion urn process is analyzed.

    Conclusions:

    • The proposed Polya urn-based segmentation method is effective for contagious phenomena.
    • The generalization allows for spatial interactions, enhancing segmentation capabilities.
    • This approach offers a robust alternative for complex image segmentation tasks.