Texture image retrieval based on contourlet-2.3 and generalized Gaussian density model

In order to improve the retrieval rate of the original contourlet transform based texture image retrieval system, a contourlet-2.3 transform based texture image retrieval system was proposed. Generalized Gaussian Density (GGD) model parameters were cascaded to form feature vectors and Kullback-Leibler distance (KLD) function was used for similarity measure. Experimental results on 640 texture images from Vistex texture image database indicate that contourlet-2.3 transform based image retrieval system is superior to that of the original contourlet transform under the same system structure with almost same length of feature vectors, retrieval time and memory needed. Furthermore, GGD combined with KLD method has higher retrieval rates than energy based features combined with Euclidean distance under comparable levels of computational complexity, decomposition parameters including the number of scale and directional subband on each scale selected in both contourlet transforms can make retrieval results quite different.