Non-parametric estimation of mixture model order

Enrique Corona, Brian Nutter, Sunanda Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publication2008 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2008 - Proceedings
Pages145-148
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2008 - Santa Fe, NM, United States
Duration: Mar 24 2008Mar 26 2008

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

Conference

Conference2008 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2008
CountryUnited States
CitySanta Fe, NM
Period03/24/0803/26/08

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