Supervised texture classification using several features extraction techniques based on ANN and SVM

Texture classification is one of the most important clues of visual processing applications .In this paper, we present a comparison between the most two popular supervised texture classification methods based on the feed forward Artificial Neural Network (ANN) and the multi-class Support Vector Machine (SVM). Five of the most common used features extraction approaches were chosen in order to extract input vectors of different sizes for both classifiers. These approaches are namely gray level histogram, edge detection, and co-occurrence matrices, besides Gabor and Biorthogonal wavelet transformations. Experiments are conducted on two different datasets the first one is engineering surface textures produced by different machining processes, and the second was taken from Brodatz (1966) textures album. The classification accuracy rate is calculated for ANN and SVM in order to measure the efficiency of each technique based on the several features extraction methods. The results show that SVM with its linear and polynomial kernels is higher in classification accuracy and faster in training time.