在常用的基于小波变换域旋转不变纹理图像检索算法中，由于存在方向信息提取有限且多尺度间系数相关性被忽略的局限性，检索效率受到影响。提出一种基于尺度相关性的渐进式旋转不变纹理图像检索算法。该算法首先采用Log-polar变换与非下采样Contourlet变换组合的方式获取具备旋转不变性的多尺度多方向变换系数，然后利用广义高斯模型拟合低通波段的全局结构信息作为粗判依据，方向子带间的尺度相关信息则采用非高斯双变量模型拟合,并作为精细渐进式检索的特征变量。基于Brodatz标准纹理库的实验结果表明，与小波变换及基于层内关系模型方法相比，该方法能以更低的特征维数获得更高的检索效率及检索准确率，是一种进行旋转纹理检索的有效手段。

During the traditional wavelet rotation-invariant texture retrieval algorithms, the extracted directional information is limited and the inter-scale dependency between the coefficients is ignored, which affects the efficiency of retrieval. In this paper, the authors propose a novel progressive rotation-invariant texture retrieval algorithm based on inter-scale dependency. Firstly, Log-polar transform and Non-subsample Contourlet transform (NSCT) are combined to acquire rotation-invariant multi-scale and multi-orientation coefficients, then generalized Gaussian distribution (GGD) model is used to extract the global structure information from low-pass coefficients which can be employed further as coarse retrieval features. Afterwards, the Non-Gaussian Bivariate Model is employed to model NSCT coefficients inter-scale dependency, which can be used as fine progressive retrieval foundations. Finally,the performance of the algorithm proposed is illustrated by experiments based on Brodatz standard texture database. Compared to inner-scale model GGD based on wavelet coefficients retrieval algorithm, our method provides better efficiency and accuracy, which is proved to be an efficient rotation-invariant texture retrieval means.