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.