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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.
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Contributor : Jakob Verbeek Connect in order to contact the contributor
Submitted on : Wednesday, February 16, 2011 - 5:04:50 PM
Last modification on : Monday, September 25, 2017 - 10:08:04 AM
Long-term archiving on: : Tuesday, May 17, 2011 - 2:37:12 AM


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  • HAL Id : inria-00321513, version 1


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



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