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Unsupervised image segmentation with Gaussian Pairwise Markov Fields

Abstract : Modeling strongly correlated random variables is a critical task in the context of latent variable models. A new probabilistic model, called Gaussian Pairwise Markov Field, is presented to generalize existing Markov Fields latent variables models, and to introduce more correlations between variables. This is done by considering the correlations within Gaussian Markov Random Fields models which are much richer than in the classical Markov Field models. The assets of the Gaussian Pairwise Markov Field model are explained. In particular, it offers a generalization of the classical Markov Field modelization that is highlighted. The new model is also considered in the practical case of unsupervised segmentation of images corrupted by long-range spatially-correlated noise, producing interesting new results.
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https://hal.archives-ouvertes.fr/hal-03104832
Contributor : Hugo GANGLOFF Connect in order to contact the contributor
Submitted on : Saturday, January 9, 2021 - 8:33:35 PM
Last modification on : Tuesday, September 27, 2022 - 4:18:15 AM
Long-term archiving on: : Saturday, April 10, 2021 - 6:23:35 PM

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Hugo Gangloff, Jean-Baptiste Courbot, Emmanuel Monfrini, Christophe Collet. Unsupervised image segmentation with Gaussian Pairwise Markov Fields. Computational Statistics and Data Analysis, Elsevier, 2021, 158, pp.107178:1-107178:20. ⟨10.1016/j.csda.2021.107178⟩. ⟨hal-03104832⟩

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