A new unsupervised image segmentation algorithm based on deterministic annealing EM

A new unsupervised image segmentation algorithm based on deterministic annealing EM (DAEM) is proposed in this paper. The method is based on maximum likelihood (ML) estimation. Image is considered as a mixture of multi-variant normal densities and the number of densities is assumed to know. In order...

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Bibliographic Details
Published inIEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003 Vol. 1; pp. 600 - 604 vol.1
Main Authors Jiaqiang Zhong, Runsheng Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 2003
Subjects
Annealing
Convergence
Image processing
Image segmentation
Maximum likelihood estimation
Parameter estimation
Partitioning algorithms
Robots
Signal processing algorithms
Thermodynamics
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ISBN078037925X
9780780379251
DOI10.1109/RISSP.2003.1285642

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Summary:A new unsupervised image segmentation algorithm based on deterministic annealing EM (DAEM) is proposed in this paper. The method is based on maximum likelihood (ML) estimation. Image is considered as a mixture of multi-variant normal densities and the number of densities is assumed to know. In order to obtain the parameters of densities, deterministic annealing EM algorithm is introduced. In DAEM algorithm, the EM process is reformulated as the problem of minimizing the thermodynamic free energy by using a statistical mechanics analogy. Thus, The DAEM algorithm can overcome the local maximize problem of general EM algorithm. The proposed method is successfully applied to image segmentation experiments.
ISBN:078037925X
9780780379251
DOI:10.1109/RISSP.2003.1285642