Texture Segmentation Using Neural Networks and Multi-scale Wavelet Features

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.

• Type: Conference
• Year: 2005
• Where: ICNC
• Authors: Tae-Hyung Kim, Il Kyu Eom, Yoo Shin Kim
• Keywords: ICNC 2005, Neural Networks, Segmentation Method, Texture Segmentation Method