Texture Classification of Gabor Filtering Images based of DST-Texton Template with LPboosting classifier
Vivek. C, Audithan. S
Journal of Information & Systems Management, Vol. 4, No. 1, 2014.
DOI: 
Link: http://www.dline.info/jism/fulltext/v4n1/2.pdf
Abstract:
In this research, the importance of having a balanced basis of texture classified images are analyzed. It obtained on brodatz images even though the latter are more numerous on the web resources. The proposed algorithm shows a way to improve performance. A textual analysis of the Gabor filtered images based on the combination of text on co-occurrence matrix with shearlet band signatures based texture classification of 40 Brodatz texture images is presented. The entropy lineage parameters of redundant and interpolate at a certain point which congregating adjacent regions based on geometric properties then the classification is apprehended by comparing the similarity between the estimated distributions of all detail sub bands through the strong LPboosting classification with various weak classifier configurations. We show that the resulted texture features while incurring the maximum of the discriminative information. Our hybrid classification method significantly outperforms the existing texture descriptors and stipulates classification accuracy in the state-of-the-art real world imaging applications.