Scale-based Gaussian coverings: combining intra and inter mixture models in image segmentation
type: Item
instance of: scholarly article
title: Scale-based Gaussian coverings: combining intra and inter mixture models in image segmentation
zbMATH Open document ID: 1179.94015
author: F. Murtagh
author: Pedro Contreras
author: J.-L. Starck
published in: Entropy
publication date: 2010-01-29T00:00:00Z
review text: Summary: By a ``covering'' we mean a Gaussian mixture model fit to observed data. Approximations of the Bayes factor can be availed of to judge model fit to the data within a given Gaussian mixture model. Between families of Gaussian mixture models, we propose the Rényi quadratic entropy as an excellent and tractable model comparison framework. We exemplify this using the segmentation of an MRI image volume, based (1) on a direct Gaussian mixture model applied to the marginal distribution function, and (2) Gaussian model fit through k-means applied to the 4D multivalued image volume furnished by the wavelet transform. Visual preference for one model over another is not immediate. The Rényi quadratic entropy allows us to show clearly that one of these modelings is superior to the other.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 94A17
Mathematics Subject Classification ID: 94A11
Mathematics Subject Classification ID: 42C40
zbMATH DE Number: 5664170
zbMATH Keywords: image segmentation
zbMATH Keywords: clustering
zbMATH Keywords: model selection
zbMATH Keywords: minimum description length
zbMATH Keywords: Bayes factor
zbMATH Keywords: Rényi entropy
zbMATH Keywords: Shannon entropy
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e11030513
OpenAlex ID: W3104446227
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DOI: 10.3390/E11030513
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description: scientific article; zbMATH DE number 5664170