The texture classification using the fusion of decisions from different texture classifiers

The improved of texture classification accuracy by using the probability weighted combination method of three texture features extraction consist of thE0020 Gray-Level Co-occurrence Matrix (GLCM), Semivariogram Function and Gaussian Markov Random Fields (GMRFs). Five different textures images are used in the experiment. The classifier that use for classify the extracted features in this research is Support Vector Machines (SVMs). The experimental result shows that the average accuracy of the combination method with probability weight up to 95.71%, which is better than the simple combination method about 2%.