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A multi-level thresholding approach using a hybrid optimal estimation algorithm
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DOI：10.1016/j.patrec.2006.11.005.png)
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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.
Keyword:
multi-level thresholding
mixture Gaussian curve fitting
expectation maximization (EM)
particle swarm optimization (PSO)
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