Unsupervised segmentation of hidden semi-Markov non-stationary chains

Jérôme Lapuyade-Lahorgue (1, 2, 3) , Wojciech Pieczynski (4, 1, 2, 3)

1 TSP - CITI - Communications, Images et Traitement de l'Information
2 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
3 CNRS - Centre National de la Recherche Scientifique
4 TIPIC-SAMOVAR - Traitement de l'Information Pour Images et Communications

The Bayesian segmentation using Hidden Markov Chains (HMC) is widely used in various domains such as speech recognition, acoustics, biosciences, climatology, text recognition, automatic translation and image processing. On the one hand, hidden semi-Markov chains (HSMC), which extend HMC, have turned out to be of interest in many situations and have improved HMC-based results. On the other hand, the case of non-stationary data can pose an important problem in real-life situations, especially when the model parameters have to be estimated. The aim of this paper is to consider these two extensions simultaneously: we propose using a particular triplet Markov chain (TMC) to deal with non-stationary hidden semi-Markov chains. In addition, we consider a recent particular HSMC having the same computation complexity as the classical HMC. We propose a related parameter estimation method and the resulting unsupervised Bayesian segmentation is validated through experiments; in particular, a real radar image segmentations are provided

Mots clés: Non-stationary hidden semi-Markov chain; Unsupervised segmentation; Iterative conditional estimation; Triplet Markov chains

Domaines: Traitement du signal et de l'image [eess.SP]

Jérôme Lapuyade-Lahorgue, Wojciech Pieczynski. Unsupervised segmentation of hidden semi-Markov non-stationary chains. Signal Processing, 2012, 92 (1), pp.29 - 42. ⟨10.1016/j.sigpro.2011.06.001⟩. ⟨hal-01347981⟩