Texture Recognition rate analysis using fusion of Gabor energy features and Simple cell features

In this paper, efforts are made to put forward a new method for Texture Recognition rate analysis using fusion of features obtained from Gabor energy features and Simple cell Response features. Different measures False positive Rate, False Negative rate and Acceptance rate are calculated for one image against database having different number of images. Experiments are carried out on Gabor Filter banks designed with different frequency ranges and comparative study is explained in detail. The best suitable range for fusion method is interpreted from result. Variation of recognition rate for different frequency ranges is also studied for both features.