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.