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Bayesian Texture Segmentation Using Multi-layer Perceptron and Markov Random Field Model  

Kim, Tae-Hyung (Dept. of Electronics Eng., Pusan Univ.)
Eom, Il-Kyu (Dept. of Electronics Eng., Pusan Univ.)
Kim, Yoo-Shin (Dept. of Electronics Eng., Pusan Univ.)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.44, no.1, 2007 , pp. 40-48 More about this Journal
Abstract
This paper presents a novel texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields in multiscale Bayesian framework. Multiscale wavelet coefficients are used as input for the neural networks. The output of the neural network is modeled as a posterior probability. Texture classification at each scale is performed by the posterior probabilities from MLP networks and MAP (maximum a posterior) classification. Then, in order to obtain the more improved segmentation result at the finest scale, our proposed method fuses the multiscale MAP classifications sequentially from coarse to fine scales. This process is done by computing the MAP classification given the classification at one scale and a priori knowledge regarding contextual information which is extracted from the adjacent coarser scale classification. In this fusion process, the MRF (Markov random field) prior distribution and Gibbs sampler are used, where the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. The proposed segmentation method shows better performance than texture segmentation using the HMT (Hidden Markov trees) model and HMTseg.
Keywords
Texture segmentation; Multiscale Bayesian framework; Neural Networks; Markov random fields; Gibbs sampler;
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