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Multiscale Bayesian texture segmentation using neural networks and Markov random fields

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Abstract

This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior probability. The multi-scale features produced by multi-level wavelet decompositions of textured images are classified at each scale by maximum a posterior (MAP) classification and the posterior probabilities from MLP networks. An MRF model is used in order to model the prior distribution of each texture class, and a factor, which fuses the classification information through scales and acts as a guide for the labeling decision, is incorporated into the MAP classification of each scale. By fusing the multi-scale MAP classifications sequentially from coarse to fine scales, our proposed method gets the final and improved segmentation result at the finest scale. In this fusion process, the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. Our texture segmentation method was applied to segmentation of gray-level textured images. The proposed segmentation method shows better performance than texture segmentation using the hidden Markov trees (HMT) model and the HMTseg algorithm, which is a multi-scale Bayesian image segmentation algorithm.

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Authors and Affiliations

  1. Agency for Defense Development, Technology 3-3, YuSeong, PO Box 35-3, DaeJeon, 305-600, South Korea

    Tae Hyung Kim

  2. Department of Electronics Engineering, Pusan National University, 30 Jangjeon-dong, Geumjeong-gu, Pusan, 609-735, South Korea

    Il Kyu Eom

  3. Research Institute of Computer, Information and Communication, Pusan National University, 30 Jangjeon-dong, Geumjeong-gu, Pusan, 609-735, South Korea

    Yoo Shin Kim

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  1. Tae Hyung Kim
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  2. Il Kyu Eom
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  3. Yoo Shin Kim
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Correspondence to Il Kyu Eom.

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Kim, T.H., Eom, I.K. & Kim, Y.S. Multiscale Bayesian texture segmentation using neural networks and Markov random fields. Neural Comput & Applic 18, 141–155 (2009). https://doi.org/10.1007/s00521-007-0167-x

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