Unsupervised image segmentation with Gaussian Pairwise Markov Fields

Hugo Gangloff, Jean-Baptiste Courbot, Emmanuel Monfrini and Christophe Collet

Computational Statistics & Data Analysis, 2021, vol. 158, issue C

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.

Keywords: Unsupervised image segmentation; Pairwise Markov Fields; Gaussian Markov Fields; Parameter estimation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:158:y:2021:i:c:s0167947321000128

DOI: 10.1016/j.csda.2021.107178

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