Texture Classification Using Gradient Features with Artificial Neural Network

Abstract

Texture Analysis is the technique usinga small number of measurable features to represent complex textures. It provides many important discriminating characteristics that are related with extracting features and coding images which are used in various patterns recognition applications and classification texture. This research studies the extraction of discriminating characteristics for various texture images from using Absolute Gradient Matrix (AGM) and a comparative study of conventional texture-analysis method and the proposed methods. Four different sets of features have been considered: the first set consists of simple features extracted from the traditional AGM, the second set consists of new features which are derived using AGM and intensity values (pAGM); the third feature set consists of two sub-sets of features extracted from two AGM calculated using two displacement values and intensity values (CtdAGM). The fourth method implies the extracted set of AGM through Artificial Neural Network (ANN) for classification purpose. The considered method was applied on 13 classes of textures belonging to three sets of Salzburg Texture Image Database (i.e., bark, marble and woven fabric), each set holds 16 images per class, thus 208 images were tested in total. The attained average accuracy of classification proved that the proposed methods were superior to the conventional texture-analysis method with respect to classification accuracy and computational complexity.