Greedy Gaussian mixture learning for texture segmentation

Abstract : The problem of segmenting an image into several modalities representing different textures can be modeled using Gaussian mixtures. Fitting a Gaussian mixtures on the data is not trivial problem and no guaranteed optimal algorithm exists. In this paper we show the benefits of a recently developed greedy procedure to Gaussian mixture learning to the problem of texture segmentation. We present the greedy learning method and provide experimental results illustrating the potential of the new method.
Type de document :
Communication dans un congrès
A. Leonardis and H. Bischof. ICANN Workshop on Kernel and Subspace Methods for Computer Vision, Aug 2001, Wien, Austria. pp.37--46, 2001
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Contributeur : Jakob Verbeek <>
Soumis le : mercredi 16 février 2011 - 17:04:50
Dernière modification le : lundi 25 septembre 2017 - 10:08:04
Document(s) archivé(s) le : mardi 17 mai 2011 - 02:37:12

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  • HAL Id : inria-00321513, version 1

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Jakob Verbeek, Nikos Vlassis, Ben Krose. Greedy Gaussian mixture learning for texture segmentation. A. Leonardis and H. Bischof. ICANN Workshop on Kernel and Subspace Methods for Computer Vision, Aug 2001, Wien, Austria. pp.37--46, 2001. 〈inria-00321513〉

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