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Authors: Mohamed El Yazid Boudaren 1 ; Emmanuel Monfrini 2 ; Kadda Beghdad Bey 1 ; Ahmed Habbouchi 1 and Wojciech Pieczynski 2

Affiliations: 1 Ecole Militaire Polytechnique, Algeria ; 2 SAMOVAR, Télécom SudParis, CNRS and Université Paris-Saclay, France

ISBN: 978-989-758-247-9

Keyword(s): Data Segmentation, Hidden Markov Chains, Nonstationary Data, Signal Processing, Triplet Markov Chains.

Related Ontology Subjects/Areas/Topics: Advanced Applications of Fuzzy Logic ; Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bioinformatics ; Biomedical Engineering ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Methodologies and Technologies ; Operational Research ; Sensor Networks ; Signal Processing ; Simulation ; Soft Computing

Abstract: An important issue in statistical image and signal segmentation consists in estimating the hidden variables of interest. For this purpose, various Bayesian estimation algorithms have been developed, particularly in the framework of hidden Markov chains, thanks to their efficient theory that allows one to recover the hidden variables from the observed ones even for large data. However, such models fail to handle nonstationary data in the unsupervised context. In this paper, we show how the recent triplet Markov chains, which are strictly more general models with comparable computational complexity, can be used to overcome this limit through two different ways: (i) in a Bayesian context by considering the switches of the hidden variables regime depending on an additional Markov process; and, (ii) by introducing Dempster-Shafer theory to model the lack of precision of the hidden process prior distributions, which is the origin of data nonstationarity. Furthermore, this study analyzes bot h approaches in order to determine which one is better-suited for nonstationary data. Experimental results are shown for sampled data and noised images. (More)

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Paper citation in several formats:
Boudaren, M.; Monfrini, E.; Beghdad Bey , K.; Habbouchi, A. and Pieczynski, W. (2017). Unsupervised Segmentation of Nonstationary Data using Triplet Markov Chains.In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-758-247-9, pages 405-414. DOI: 10.5220/0006276704050414

@conference{iceis17,
author={Mohamed El Yazid Boudaren. and Emmanuel Monfrini. and Kadda Beghdad Bey . and Ahmed Habbouchi. and Wojciech Pieczynski.},
title={Unsupervised Segmentation of Nonstationary Data using Triplet Markov Chains},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},
year={2017},
pages={405-414},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006276704050414},
isbn={978-989-758-247-9},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - Unsupervised Segmentation of Nonstationary Data using Triplet Markov Chains
SN - 978-989-758-247-9
AU - Boudaren, M.
AU - Monfrini, E.
AU - Beghdad Bey , K.
AU - Habbouchi, A.
AU - Pieczynski, W.
PY - 2017
SP - 405
EP - 414
DO - 10.5220/0006276704050414

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