Wavelets and support vector machines for texture classification
We present a novel texture classification algorithm using 2-D discrete wavelet transform (DWT) and support vector machines (SVM). The DWT is used to generate feature images from individual wavelet subbands, and a local energy function is computed corresponding to each pixel of the feature images. This feature vector is first used for training and later on for testing the SVM classifier. The experimental setup consists of images from the Brodatz and MlT VisTeX texture databases and a combination of some images therein. The proposed method produces promising classification results for both single and multiple class texture analysis problems.