Dual Tree Complex Contourlet Texture Image Retrieval

Contourlet transform has better performance in directional information representation than wavelet transform and has been studied by many researchers in retrieval systems and has been shown that it is superior to wavelet ones at retrieval rate. In order to improve the retrieval rate further, a dual-tree complex contourlet transform based texture image retrieval system was proposed in this paper. In the system, the dual tree contourlet transform was used to transform each image into contourlet domain and implemented multiscale decomposition, sub-bands energy and standard deviations in contourlet domain are cascaded to form feature vectors, and the similarity metric used here is Canberra distance. Experimental results show that dual tree contourlet transform based image retrieval system is superior to those of the original contourlet transform, non-subsampled contourlet transform, semi-subsampled contourlet transform, contourlet-2.3 and contourlet-1.3 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.