EBEM: An Entropy-based EM Algorithm for Gaussian Mixture Models

Authors:
Antonio Penalver Benavent;Francisco Escolano Ruiz;Juan M. Saez Martinez
Affiliations:
Robot Vision Group Alicante University;Robot Vision Group Alicante University;Robot Vision Group Alicante University
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

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Pattern Recognition

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Abstract

In this paper we address the problem of estimating the parameters of a Gaussian mixture model. Although the EM algorithm yields the maximum-likelihood solution it requires a careful initialization of the parameters and the optimal number of kernels in the mixture may be unknown beforehand. We propose a criterion based on the entropy of the pdf (probability density function) associated to each kernel to measure the quality of a given mixture model. A novel method for estimating Shannon entropy based on Entropic Spanning Graphs is developed and a modification of the classical EM algorithm to find the optimal number of kernels in the mixture is presented. We test our algorithm in probability density estimation, pattern recognition and color image segmentation.