Comparison of Classification Methods with Second Order Statistical Analysis and Wavelet Transform for Texture Image Classification

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Abstract

In computer vision system, texture refers to the characteristics of an object that appear on its surface. Texture classification is to classify textures in correct texture groups. The accuracy of texture image classification depends on quality of texture features and classification algorithm used. In this paper, Brodatz texture images are used as an experimental data. Features are extracted from texture images using Gray Level Co -- occurrence method (GLCM) and two-level, two-dimensional discrete wavelet transform (2D DWT). The extracted features are applied to the k-nearest neighbor (k - NN), support vector machine (SVM) and feed forward back propagation neural network (FFBPNN) classifiers. Haralick features are found more suitable for Brodatz texture image classification than wavelet features. It is found that Neural network classifier performs better than k -- Nearest Neighbour classifier and Support Vector Machine classifier for the selected Brodatz texture images.

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