Texture segmentation using neural networks and multi-scale wavelet features

Authors:
Tae Hyung Kim;Il Kyu Eom;Yoo Shin Kim
Affiliations:
Dept. Electronics Engineering, Pusan National University, Busan, Republic of Korea;Dept. Information and Communications Engineering, Miryang National University, Miryang-si, Gyeongsangnam-do, Republic of Korea;Research Institute of Computer, Information and Communication, Pusan National University, Busan, Republic of Korea
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

This paper presents a novel texture segmentation method using Bayesian estimation and neural networks. Multi-scale wavelet coefficients and the context information extracted from neighboring wavelet coefficients were used as input for the neural networks. The output was modeled as a posterior probability. The context information was obtained by HMT (Hidden Markov Trees) model. The proposed segmentation method shows performed better than ML (Maximum Likelihood) segmentation using the HMT model.