由于在频域用能量参数来表示图像的特征矢量缺乏准确性，而且实数离散小波变换具有平移变化性和弱的方向选择性，为此针对以上问题提出了一种基于复数小波域广义高斯分布模型的纹理图像检索方法。该方法首先利用双树复数小波变换系数的统计特性来建立广义高斯分布的统计模型；然后基于该模型提取图像的特征矢量；最后利用Kullback Leibler distance(KLD)测度算法进行纹理图像检索。对Brodatz图像库的仿真表明，新方法较双树复数小波算法的查准率提高6.96%，较基于Gabor纹理特征检索法的查准率提高了18.8%。同时复数小波系数统计模型具有旋转不变性。新方法对今后的纹理图像检索具有重要的理论与实际意义。

A novel texture image retrieval approach based on generalized Gaussian distribution statistic model in the complex wavelet domain is presented for common representation of texture that lacks precision and real DWT that have shift sensitivity and poor directionality. Generalized Gaussian distribution statistic model is constructed by taking advantage of the statistical attribution of complex wavelet transformation coefficients. Image feature is obtained by using this new model. A texture image retrieval project is designed based on new statistic model combining with Kullback Leibler distance (KLD). Extensive experiments from Brodatz texture images clearly show the superiority of the novel approach which obtained accuracy 6.96 percent higher than method that is based on complex wavelet transform,and 18.6 percent more than method that is based on Gabor texture features. So the new method is valuable for texture image retrieval in the future.