Complex contourlet texture retrieval system using new combinational features

Contourlet Texture image retrieval system using the features combined by energy and standard deviation of subband coefficients has better performance than the same structure system based on wavelet transform due to contourlet has better directional information representation than wavelet transform. In order to improve the retrieval rate further, a new texture image retrieval system was proposed in this paper. In the system, the dual tree contourlet transform was used to transform each image into complex contourlet domain and implemented multiscale decomposition, sub-bands energy, standard deviations and kurtosis in contourlet domain were cascaded to form feature vectors, and the similarity metric used here is Canberra distance. Experimental results on brodatz test images set show that the new dual tree contourlet transform based image retrieval system is superior to those of the original contourlet transform, non-subsampled contourlet transform under the same system structure with almost same length of feature vectors, retrieval time and memory needed; and contourlet decomposition structure parameter can make significant effects on retrieval rates, especially scale number.