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Texture segmentation using Neural Networks and multi-scale Bayesian image segmentation technique  

Kim Tae-Hyung (Dept. of Electronics Eng., Pusan Univ.)
Eom Il-Kyu (Dept. of Information and Communication Eng., Miryang Univ.)
Kim Yoo-Shin (Dept. of Electronics Eng., Pusan Univ.)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.42, no.4, 2005 , pp. 39-48 More about this Journal
Abstract
This paper proposes novel texture segmentation method using Bayesian estimation method and neural networks. We use multi-scale wavelet coefficients and the context information of neighboring wavelets coefficients as the input of networks. The output of neural networks is modeled as a posterior probability. The context information is obtained by HMT(Hidden Markov Tree) model. This proposed segmentation method shows better performance than ML(Maximum Likelihood) segmentation using HMT model. And post-processed texture segmentation results as using multi-scale Bayesian image segmentation technique called HMTseg in each segmentation by HMT and the proposed method also show that the proposed method is superior to the method using HMT.
Keywords
Texture segmentation; Neural Networks; Wavelets; Multi-scale Bayesian image segmentation; Hidden Markov Trees;
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