Texture Image Retrieval Based on Contourlet Transform Using Generalized Gaussian Model
基于Contourlet广义高斯模型的纹理图像检索

Combining non-separable and directional filters banks,contourlet transform can effectively capture more edges and contours in natural images than wavelets do due to its capability of representing directional information.This paper casts light on the statistical features of contourlet coefficients,according to which we set up a model using Generalized Gaussian Density Function.To test this model,we applied it in texture images selected from VisTex database.After the extraction of model parameters using moment matching method,Kullback-Leibler(K-L) Distance is used to measure the similarity between images.Experiments on 800 texture images demonstrate that the average retrieval rate using our method is about 2% to 10% higher than that of wavelet method.The method proposed improves the extraction of directional textures.