Rotated complex wavelet based texture features for content based image retrieval

In this paper we have proposed a novel approach of extracting texture features for content-based image retrieval. A new set of two-dimensional (2-D) rotated complex wavelet filters (RCWF) is designed with complex wavelet filter coefficients. 2-D RCWF are nonseparable and oriented, which improves characterization of oriented textures. Dual-tree rotated complex wavelet filter (DT-RCWF) and dual-tree complex wavelet transform (DT-CWT) are used jointly for texture analysis in twelve different directions. Texture features are obtained by computing the energy and standard deviation of each subband. Retrieval results obtained using each individual method and in combination are presented. Retrieval performance obtained with the combined filterbank is superior relative to the performance obtained using the other existing methods. New method also retains comparable levels of computational complexity.