Neural network based texture segmentation using a markov random field model

  • Authors:

  • Tae Hyung Kim;Hyun Min Kang;Il Kyu Eom;Yoo Shin Kim

  • Affiliations:

  • Dept. Electronics Engineering, Pusan National University, Busan, Republic of Korea;Dept. Electronics Engineering, Pusan National University, Busan, Republic of Korea;Dept. Electronics Engineering, Pusan National University, Busan, Republic of Korea;Research Institute of Computer, Information and Communication, Pusan National Univesity, Busan, Republic of Korea

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
  • Year:
  • 2006

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Abstract

This paper presents a novel texture segmentation method using neural networks and a Markov random field (MRF) model. Multi-scale wavelet coefficients are used as input for the neural networks. The output of the neural network is modeled as a posterior probability. Initially, the multi-scale texture segmentation is performed by the posterior probabilities from the neural networks and MAP (maximum a posterior) classification. Then the MAP segmentation maps are produced at all scales. In order to obtain the more improved segmentation result at the finest scale, our proposed method fuses the multi-scale MAP segmentations sequentially from coarse to fine scales. This is done by computing the MAP segmentation given the segmentation map at one scale and a priori knowledge regarding contextual information which is extracted from the adjacent coarser scale segmentation. In this fusion process, the MRF 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.