A Comparative Analysis of Texture Methods for Visual Object Categorization

This paper presents a comparative study to the most common texture features analysis methods. In fact, there are two kinds of approaches have been proposed to extract the texture features for the purpose of object categorization, the former deals with the intensity pixels which derived the intensities texture and the second method dealing with the edge pixels which obtained the edge texture. However, to extract and make a comparative analysis to the texture maps there are several approaches have been presented in this research, i.e., Gradient, Co-occurrence matrix, Contrast and Edge Histogram Descriptor. A real world images dataset denoted by Caltech 101 dataset has been adopted to evaluate the proposed texture analysis methods. Mostly, the first 20 classes with 40 images per-class have been chosen to demonstrate the methods performance. Fundamentally, the objects of these images have almost isolated for the purpose of categorization. The experiment results show that the edge histogram descriptor outperformed the other proposed texture analysis methods with average accuracy 71.175±1.355775 because the edge histogram descriptor is less sensitive to the noise and variation of pixels intensities which most of the objects of Caltech 101 dataset profoundly affected.