This paper presents a contextual algorithm for the computation of Baum’s forward and backward probabilities, which are intensively used in the framework of Hidden Markov Chain (HMC) models. The method differs from the original algorithm since it only takes into account a neighborhood of limited length and not all the chain for computations. Comparative experiments with respect to the neighborhood size have been conducted on both Markovian (simulations) and not Markovian (images) data, by mean of supervised and unsupervised classifications.
['Stéphane Derrode', 'Lamia Benyoussef', 'Wojciech Pieczynski']
Contextual Estimation Of Hidden Markov Chains With Application To Image Segmentation