| Paper: | MLSP-P6.2 | ||
| Session: | Learning Theory and Models | ||
| Time: | Thursday, May 20, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Bayesian Learning and Modeling | ||
| Title: | UNSUPERVISED IMAGE SEGMENTATION BASED ON HIGH-ORDER HIDDEN MARKOV CHAINS | ||
| Authors: | Stéphane Derrode; GSM, Inst. Fresnel (CNRS - UMR 6133) | ||
| Cyril Carincotte; GSM, Inst. Fresnel (CNRS - UMR 6133) | |||
| Salah Bourennane; GSM, Inst. Fresnel (CNRS - UMR 6133) | |||
| Abstract: | First order hidden Markov models have been used for a long time in image processing, especially in image segmentation. In this paper, we propose a technique for the unsupervised segmentation of images, based on high-order hidden Markov chains. We also show that it is possible to relax the classical hypothesis regarding the state observation probability density, which allows to take into account some particular correlated noise. Model parameter estimation is performed from an extension of the general Iterative Conditional Estimation (ICE) method that takes into account the order of the chain. A comparative study conducted on a simulated image is carried out according to the order of the chain. Experimental results on Synthetic Aperture Radar (SAR) images show that the new approach can provide a more homogeneous segmentation than the classical one, implying higher complexity algorithm and computation time. | ||
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