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Unsupervised multiscale color image segmentation based on MDL principle.

Qiming Luo1, Taghi M Khoshgoftaar

  • 1Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA. qluo@fau.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 5, 2006
PubMed
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This study introduces an unsupervised algorithm for color image segmentation using mean shift clustering and region merging. The method optimizes the minimum description length criterion, showing competitive performance on standard benchmarks.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Image segmentation is crucial for image analysis.
  • Existing methods often require supervision or struggle with scale variations.

Purpose of the Study:

  • To develop an unsupervised algorithm for multiscale color image segmentation.
  • To improve segmentation accuracy by merging regions based on a defined criterion.

Main Methods:

  • Applied mean shift clustering for initial over-segmentation.
  • Utilized region merging at multiple scales.
  • Minimized the minimum description length (MDL) criterion for optimal merging.

Main Results:

  • Achieved effective over-segmentation using mean shift clustering.

Related Experiment Videos

  • Successfully merged regions across multiple scales.
  • Demonstrated favorable performance compared to existing methods on the Berkeley segmentation benchmark.

    • Conclusions:

    • The proposed unsupervised multiscale approach offers a robust solution for color image segmentation.
    • Minimizing the MDL criterion effectively guides the region merging process.
    • The algorithm shows promise for various image analysis applications.