Texture image retrieval algorithm with dual tree complex contourlet and three statistical features

Texture image retrieval system using contourlet transform has better performance than the same structure system based on wavelet transform due to contourlet´s better directional information representation than wavelet transform. 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, standard deviations and skewness 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 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.