Pattern recognition using dual-tree complex wavelet features and SVM

Abstract

A novel descriptor for pattern recognition is proposed by using dual tree complex wavelet features and SVM. The approximate shift-invariant property of the dual tree complex wavelet and its good directional selectivity in 2D make it a very appealing choice for pattern recognition. Recently, SVM has been shown to be very successful in pattern recognition. By combining these two tools we find that better recognition results are obtained. The recognition rate of similar patterns improves from 94% to 99.25% for both the Gaussian kernel and the wavelet kernel. It is concluded that the dual-tree complex wavelet should be used instead of the scalar wavelet for pattern recognition applications