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Varying complexity in tree-structured image distribution models.

Clay Spence1, Lucas C Parra, Paul Sajda

  • 1Sarnoff Corporation, Princeton, NJ 08540, USA. cspence@sarnoff.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 17, 2006
PubMed
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We propose the hierarchical image probability (HIP) model for advanced image analysis. This complex tree-structured model improves upon the hidden Markov tree (HMT) for image classification, synthesis, and compression.

Area of Science:

  • Computer Vision
  • Image Processing
  • Statistical Modeling

Background:

  • Probabilistic models of image statistics are crucial for image analysis.
  • Tree-structured models, like the hidden Markov tree (HMT), offer exact computations for finite hidden states.
  • Existing models like HMT may limit complexity, potentially hindering performance.

Purpose of the Study:

  • To introduce a more complex tree-structured model for image statistics.
  • To utilize information-theoretic penalties for selecting model complexity.
  • To demonstrate the utility of enhanced tree-structured models in image analysis tasks.

Main Methods:

  • Developed the hierarchical image probability (HIP) model, a more complex tree-structured model.
  • Employed multivariate Gaussians for wavelet coefficient distributions.

Related Experiment Videos

  • Used varying numbers of hidden states per resolution.
  • Applied information-theoretic penalties for model selection.

    • Main Results:

    • The HIP model, utilizing multivariate Gaussians and flexible hidden states, showed broad utility.
    • Demonstrated effectiveness in image classification, synthesis, and compression.
    • Compared HIP model performance against the hidden Markov tree (HMT) across various image types.

    Conclusions:

    • Allowing greater complexity in tree-structured models, as exemplified by HIP, is beneficial.
    • The HIP model offers a powerful alternative to HMT for diverse image analysis applications.
    • Information-theoretic penalties are effective for selecting optimal model complexity.