Aggregate features approach for texture analysis

Abstract

Texture analysis is significant field in image processing and computer vision. Shape and texture has groovy correlation and texture can be defined by shape descriptor. Three individual approach Zernike moment, which is orthogonal shape signifier, Gabor features and Haralick features are utilized for texture analysis. Another approach is applied by aggregating all the features for texture analysis. Texture is defined by features which are extracted using Gabor filter, GLCM and Zernike moments. Classification of texture are done using back-propagation neural network. Individual approach is applied on texture images and accuracy is determined. By combining all approaches overall result is improved.