MRF-based texture segmentation using wavelet decomposed images

In recent textured image segmentation, Bayesian approaches capitalizing on computational efficiency of multiresolution representations have received much attention. Most of the previous researches have been based on multiresolution stochastic models which use the Gaussian pyramid image decomposition...

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Bibliographic Details
Published in:Pattern recognition 2002-04, Vol.35 (4), p.771-782
Main Authors: Noda, Hideki, Shirazi, Mahdad N., Kawaguchi, Eiji
Format: Article
Language:English
Subjects:
Image segmentation
MRF
Multiresolution
Texture
Unsupervised
Wavelet
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Summary:In recent textured image segmentation, Bayesian approaches capitalizing on computational efficiency of multiresolution representations have received much attention. Most of the previous researches have been based on multiresolution stochastic models which use the Gaussian pyramid image decomposition. In this paper, motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we present an unsupervised textured image segmentation algorithm based on a multiscale stochastic modeling over the wavelet decomposition of image. The model, using doubly stochastic Markov random fields, captures intrascale statistical dependencies over the wavelet decomposed image and intrascale and interscale dependencies over the corresponding multiresolution region image.
ISSN:0031-3203
1873-5142
DOI:10.1016/S0031-3203(01)00077-2