Abstract
Experimental results of using various texture features based on Support Vector Machine (SVM) and Self-Organizing Map (SOM) are reported in this thesis. For classification, the texture features are derived from Gabor and four wavelet transforms (9/7, 5/3, Daubechies’ four, and Haar transforms). Then, the performance of various texture features will be evaluated by SVM and SOM. Moreover, a comparison of SVM and SOM for texture classification will be presented and illustrated in the final experimental results. Besides, the texture features for SVM and SOM have worked from a lightly different viewpoint; the training data with scaling or non-scaling process may heavily affect the classification rate. Our database consists of 96 classes as homogeneous as possible (1536 images of size 128×128) from Brodatz’s album. So the performance evaluation of various texture features with SVM and SOM will be tested on our database and be reported in the experimental results.