EM procedures using mean field-like approximations for Markov model-based image segmentation
✍ Scribed by Gilles Celeux; Florence Forbes; Nathalie Peyrard
- Publisher
- Elsevier Science
- Year
- 2003
- Tongue
- English
- Weight
- 484 KB
- Volume
- 36
- Category
- Article
- ISSN
- 0031-3203
No coin nor oath required. For personal study only.
✦ Synopsis
Image segmentation using Markov random ÿelds involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, di culties arise due to the dependence structure in the models and approximations are required. Using ideas from the mean ÿeld approximation principle, we propose a class of EM-like algorithms in which the computation reduces to dealing with systems of independent variables. Within this class, the simulated ÿeld algorithm is a new stochastic algorithm which appears to be the most promising for its good performance and speed, on synthetic and real image experiments.