Classification of Texture Images Based on the Histogram of Oriented Gradients Using Support Vector Machines

Abstract

Herein, using support vector machines, texture images were classified based on the histogram of oriented gradients, from which feature vectors were obtained. In addition, the success rate was examined for the feature vectors with different dimensions and the minimum length of a feature vector for performing classification was determined to be 288 elements.