Color image segmentation using gaussian mixtures and particle swarm optimization

Authors:
Wesley Martins Teles;Carlos Henrique Quartucci Forster
Affiliations:
Program of Electronic & Comp. Eng., Inst. Tecn. de Aeronáutica (ITA), São José dos Campos, State of São Paulo, Brazil,Univ. Estadual de Goiás (UEG), Unidade Univ. de E ...;Program of Electronic & Computer Engineering, Instituto Tecnológico de Aeronáutica (ITA), São José dos Campos, State of São Paulo, Brazil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

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Abstract

The model of Gaussian Mixture is particularly useful to perform unsupervised learning. Currently, the principal technique to estimate the mixture parameters is the Expectation Maximization method which has a great chance of obtaining sub-optimal results. In this work we opted, instead, for the Particle Swarm Optimization as an alternative way to estimate parameter of Gaussian Mixture applied to multivariate data, which has greater chance of reaching the optimum. To evaluate the proposed approach, color images from fluorescence microscopy are segmented considering the 3D color space. Some particular features of this kind of color image are also considered to improve the performance of the search.