Rotation-invariant Texture Retrieval Based on Complementary Features

Among the traditional texture image retrieval algorithms based on wavelet transform, the limitation that the correlation between scales and subbands is neglected leads to the poor retrieval efficiency. This paper proposed a novel rotation-invariant texture retrieval method which is based on complementary features. It firstly models the coefficients of subbands with alpha-stable distribution and uses the fractional lower-order moment (FLOM) to capture the sub-Gaussian properties. Then estimate the so-called covariations between orientation subbands as characteristic vectors of images. The next step is to construct a steerable multivariate sub-Gaussian model and deduce the rotation-invariant characteristic expression. Meanwhile make the low frequency energy statistics as part of the characteristics. Finally we choose a suitable distance function to measure the similarity between two images. The experimental results show that this method describes more image information, and it achieve a higher retrieval accuracy and it is a kind of effective way of rotation-invariant texture image retrieval.