Texture Classification Using Curvelet Transform

Texture classification has played an important role in many real life applications. Now, classification based on wavelet transform is being very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, ridgelet transform which deal effectively with line singularities in 2-D is introduced. But images often contain curves rather than straight lines, so curvelet transform is designed to handle it. It allows representing edges and other singularities along lines in a more efficient way when compared with other transforms. In this paper, the issue of texture classification based on curvelet transform has been analyzed. One group feature vector can be constructed by the mean and variance of the curvelet statistical features, which are derived from the sub-bands of the curvelet decomposition and are used for classification. Experimental results show that this approach allows obtaining high degree of success rate in classification.