A NOVELTY APPROACH OF SPATIAL CO-OCCURRENCE AND DISCRETE SHEARLET TRANSFORM BASED TEXTURE CLASSIFICATION USING LPBOOSTING CLASSIFIER

Abstract

Recently, the research towards Brodatz database for texture classification done at considerable amount of study has been published, the effective classification are vulnerable towards for training and test sets. This study presents the novel texture classification method based on feature descriptor, called spatial co-occurrence with discrete shearlet transformation through the LPboosting classification. It can be considered as a frame through the texton template that mapped into the texture images and it works directly on relating the adjacent spatial with its pixel boundary through the local intensity order. Hence, the proposed method for the feature extraction and classification of texture suggested with the experimentation through the spatial co-occurrence matrix with the power spectrum based discrete shearlet transform and it classified through the LP boosting method on Brodatz database images. This hybrid second order statistical based classification method significantly outperforms the existing texture descriptors the multiscale geometric tool shows the proposed method outperforms other classification method.