Rotation-invariant texture retrieval with Gaussianized steerable pyramids

This paper presents a novel rotation-invariant image retrieval scheme based on steerable pyramid transforms. First, we model the subband coefficients as sub-Gaussian random vectors to capture their non-Gaussian behavior. Then, we apply a normalization process in order to Gaussianize the coefficients. As a result, the feature extraction step consists of estimating the covariances between the normalized pyramid coefficients. The similarity of two distinct images is measured by minimizing the Kullback-Leibler divergence (KLD) between their corresponding multivariate Gaussian distributions, where the minimization is performed over a set of rotation angles. We provide analytical expressions for the minimum KLD and we demonstrate the effectiveness of our proposed method using a set of real texture images.