Comparison of wavelet-SVM and wavelet-adaptive network based fuzzy inference system for texture classification

Abstract

Texture image is a low-level image feature. Nowadays, there are many different applications involving texture analysis, including endoscope, ultrasonographic images recognition, document classification, radar image classification, and texture-based image retrieval. The texture images analysis and classification becomes an important topic. The greatest hardness of texture analysis and classification in the past was the deficiency of enough methods to characterize. Multi-resolution analysis methods such as wavelet and wavelet packet decompositions are more superior to other classic statistical methods. In this paper, a comparison of wavelet-support vector machine (W-SVM) and wavelet-adaptive network based fuzzy inference system (W-ANFIS) approaches for texture image classification is presented. Both W-SVM and W-ANFIS methods are used for classification of the 22 texture images obtained from Brodatz image album. There, 50 64 x 64 image regions were randomly selected (overlapping or nonoverlapping) for each of these 22 images. 25 of these image regions and other 25 of these image regions are used for training and testing of the W-SVM and W-ANFIS methods, respectively.