Texture classification using ridgelet transform

Texture classification has long been an important research topic in image processing. Classification based on the wavelet transform has become very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, a ridgelet transform which deals effectively with line singularities in 2-D is introduced. It allows representing edges and other singularities along lines in a more efficient way. In this paper, the issue of texture classification based on a ridgelet transform has been analyzed. Features are derived from sub-bands of the ridgelet decomposition and are used for classification for a data set containing 20 texture images. Experimental results show that this approach allows to obtain a high degree of success in classification.