A multi-level thresholding approach using a hybrid optimal estimation algorithm

Shu‐Kai S. Fan

doi:10.1016/j.patrec.2006.11.005

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A multi-level thresholding approach using a hybrid optimal estimation algorithm

2007-04-01

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Shu‐Kai S. Fan *

Yen-Ting Lin

DOI：10.1016/j.patrec.2006.11.005

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Abstract

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This paper presented a hybrid optimal estimation algorithm for solving multi-level thresholding problems in image segmentation. The distribution of image intensity is modeled as a random variable, which is approximated by a mixture Gaussian model. The Gaussian's parameter estimates are iteratively computed by using the proposed PSO + EM algorithm, which consists of two main components: (1) global search by using particle swarm optimization (PSO); (ii) the best particle is updated through expectation maximization (EM) which leads the remaining particles to seek optimal solution in search space. In the PSO + EM algorithm, the parameter estimates fed into EM procedure are obtained from global search performed by PSO, expecting to provide a suitable starting point for EM while fitting the mixture Gaussians model. The preliminary experimental results show that the hybrid PSO + EM algorithm could solve the multi-level thresholding problem quite swiftly, and also provide quality thresholding outputs for complex images. (c) 2006 Elsevier B.V. All rights reserved.

Keywords:

multi-level thresholding

mixture Gaussian curve fitting

expectation maximization (EM)

particle swarm optimization (PSO)

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