Texture classification by multi-model feature integration using Bayesian networks

In this paper, a texture classification method based on multi-model feature integration by Bayesian networks is proposed. Considering that many image textures exhibit both structural and statistical properties, two feature sets based on two texture models––the Gabor model and the Gaussian Markov random field model are used to describe the image properties in both structure and statistics. A Bayesian network classifier is then used to combine these two sets of features along with their individual confidence measures for texture classification. Seventy eight Brodatz textures were used to evaluate the classification performance. The results show that the proposed method is better than that using a single set of features from either model for texture classification. 2002 Elsevier Science B.V. All rights reserved.