Texture Recognition Using Gabor Filter for Extracting Feature Vectors With the Regression Mining Algorithm

Abstract

This article considered only natural types of texture and then applying the Gabor filter for better classifications. The concept used is to discard the stochastic features to avoid any mixing of feature vector while it is extracting from the image dataset. The proposed approach has considered the Gabor filter for texture recognition primarily but with the combined method of spatial width and orientation to get the optimal alignment, this optical alignment mine the maximum feature vector by applying the REP algorithm over the data mined from the texture. This will result in better accuracy in the results. Initially, the frequency response over the surface due to applying Gabor filter has been calculated and then the work proceeded in a manner that first natural images are loaded into the MATLAB tool then it is preprocessed, and then final classifications are performed for final results. The primarily concentrated over texture information of image datasets rather than the multispectral information along with REP regression algorithm to do actual mining of feature vectors. Unlike the conventional approach of the Gabor filter, this article focuses on the variance and spatial relationship between two or more than two pixels. The deviation calculated is used for normalizing the feature vectors, and the accuracy can be hence increase using the proposed commuted technique.