A generative probabilistic oriented wavelet model for texture segmentation

Stainvas, Inna and Lowe, David (2003). A generative probabilistic oriented wavelet model for texture segmentation. Neural Processing Letters, 17 (3), pp. 217-238.


This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.

Publication DOI: https://doi.org/10.1023/A:1026089427119
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: The original publication is available at www.springerlink.com
Uncontrolled Keywords: bayesian networks and ensembles,dyadic wavelet transform,gabor wavelet transform,generative probabilistic model,image texture segmentation,novelty detection,Artificial Intelligence,Neuroscience(all)
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... 3A1026089427119 (Publisher URL)
Published Date: 2003-06
Authors: Stainvas, Inna
Lowe, David



Version: Accepted Version

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