Texture Feature Extraction Inspired by Natural Vision System and HMAX Algorithm

In this paper, a new and effective method called HMAX is used for image texture. feature extraction. This method is inspired by the biological system of brain and human vision in order to create feature vectors for image recognition. A set of C2 features obtained from HMAX algorithm that are stable against changes in angle and size, are extracted from all image datasets firstly. Then using artificial neural networks and Knearest neighbor classifiers, eight different types of natural texture images from VISTEX dataset are classified. In order to evaluate the HMAX feature extraction method, the classification results are compared with Gabor filter banks. Since HMAX model is consistent with natural vision system, it is expected to obtain a better accuracy compared to Gabor filter banks. Experimental results with artificial neural network and K-nearest neighbor classifier show that the accuracy of 90.12% and 84.50% respectively for HMAX features. They have significant improvements compared to Gabor filter banks which obtained 78.62% and 72% accuracy.