Texture classification using Shearlet transform and GLCM
Khatere Meshkini, Hassan Ghassemian · 2017
Texture is one of the most important and effective element in image recognition and image processing. There are a lot of procedures in texture classification, recent researches are based on different transforms such as Ripplet transform. In this paper textured images are classified using Shearlet transform. Shearlet transforms provide a general framework for analyzing and representing data with anisotropic information at multiple scales. As a consequence, signal singularities, such as edges, can be precisely detected and located in images. In the present research we have used GLCM and Shearlet transform to extract texture features in order to classify textured images. In this method first Shearlet coefficients and co-occurrence features extract from textured images then we classify textures using inner product of Shearlet coefficient and co-occurrence features. The performance of the proposed feature set is evaluated on Brodatz texture album. Experimental results demonstrate the superiority of the proposed descriptor as compared to other methods considered in this paper.