Rotation-Invariant Texture Retrieval with Gaussianized Steerable Pyramids

This paper presents a novel rotation-invariant image retrieval scheme based on a transformation of the texture information via a steerable pyramid. First, we fit the distribution of the subband coefficients using a joint alpha-stable sub-Gaussian model 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 between two distinct texture images is measured by minimizing a rotation-invariant version of the Kullback-Leibler Divergence between their corresponding multivariate Gaussian distributions, where the minimization is performed over a set of rotation angles.


Published in:
IEEE Transactions on Image Processing, 15, 9, 2702-2718
Year:
2006
Laboratories:




 Record created 2005-04-20, last modified 2018-03-17

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