Non-parametric Estimation of Mixture Model Order

  • Authors:

  • Enrique Corona;Brian Nutter;Sunanda Mitra

  • Affiliations:

  • Department of Electrical and Computer Engineering, Texas Tech University, enrique.corona@ttu.edu;Department of Electrical and Computer Engineering, Texas Tech University, brian.nutter@ttu.edu;Department of Electrical and Computer Engineering, Texas Tech University, sunanda.mitra@ttu.edu

  • Venue:
  • SSIAI '08 Proceedings of the 2008 IEEE Southwest Symposium on Image Analysis and Interpretation
  • Year:
  • 2008

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Abstract

Mixture models are among the most popular and effective techniques for image segmentation. While Gaussian Mixture Models (GMM) are a reasonable choice, the number of components is not easy to determine. A non-parametric technique, based on the transformation and analysis of the D(R) (distortion-rate) curve is proposed for model order identification purposes. This curve is estimated via the popular K-means clustering algorithm. To achieve repeatability and efficiency, various centroid initialization and image down sampling methods are proposed and tested. This technique also provides good starting points for inferring the GMM parameters via the expectation-maximization (EM) algorithm, which effectively reduces the segmentation time and the chances of getting trapped in local optima.