Rotation-invariant texture retrieval with Gaussianized steerable pyramids.

George Tzagkarakis; Baltasar Beferull-Lozano; Panagiotis Tsakalides

doi:10.1109/tip.2006.877356

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Rotation-invariant texture retrieval with Gaussianized steerable pyramids.

George Tzagkarakis·Baltasar Beferull-Lozano·Panagiotis Tsakalides

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Abstract

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.

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Algorithms Artificial Intelligence Computer Simulation Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Models, Statistical Normal Distribution Pattern Recognition, Automated Evaluation Study Journal Article Research Support, Non-U.S. Gov't

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George Tzagkarakis, Baltasar Beferull-Lozano, Panagiotis Tsakalides. 2006. Rotation-invariant texture retrieval with Gaussianized steerable pyramids.. https://doi.org/10.1109/tip.2006.877356

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