Texture classification using dual-tree complex wavelet transform

A new texture feature extraction method utilizing the dual-tree complex wavelet transform (DT-CWT) is introduced. The complex wavelet transform is a tool that uses a dual tree of wavelet filters to find the real and imaginary parts of complex wavelet coefficients. The approximate shift invariance, good directional selectivity, and computational efficiency properties of the DT-CWT make it a good candidate for representing the texture features. We propose a method for efficiently using the properties of the DT-CWT in finding the directional and spatial/frequency characteristics of the patterns and classifying different texture patterns in terms of these characteristics. Experimental results show that the proposed feature extraction and classification method is efficient in terms of the computational speed and retrieval accuracy.