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Bayes risk weighted vector quantization with posterior estimation for image compression and classification.

K O Perlmutter1, S M Perlmutter, R M Gray

  • 1Dept. of Electr. Eng., Stanford Univ., CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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This study introduces a novel vector quantization (VQ) approach for simultaneous image compression and classification. The method enhances abnormality detection in medical images while maintaining high compression efficiency.

Area of Science:

  • Digital Image Processing
  • Machine Learning
  • Information Theory

Background:

  • Compression and classification are crucial for digital information.
  • Combined approaches are valuable for applications like medical image abnormality detection.
  • Vector quantization (VQ) offers a promising framework for integrated compression and classification.

Purpose of the Study:

  • To investigate VQ-based algorithms for minimizing image distortion and classification errors simultaneously.
  • To develop and evaluate a nonparametric technique for combined compression and classification.
  • To introduce a tree-structured posterior estimator for Bayes risk computation in VQ design.

Main Methods:

  • Exploration of VQ-based algorithms with full search and tree-structured codes.

Related Experiment Videos

  • Implementation of a nonparametric technique incorporating Bayes risk into the distortion measure.
  • Development of a tree-structured posterior estimator for class posterior probabilities.

    • Main Results:

    • The proposed system achieves superior classification accuracy.
    • Compression performance is comparable or superior to existing VQ-based methods.
    • Demonstrated effectiveness on two distinct image sources.

    Conclusions:

    • The developed VQ system effectively balances image compression and classification accuracy.
    • The nonparametric approach with Bayes risk offers a significant advancement.
    • This method shows potential for improved medical image analysis and other related fields.