%0 Conference Paper %1 Tzagkarakis2004 %A Tzagkarakis, G. %A Beferull-Lozano, B. %A Tsakalides, P. %B Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on %D 2004 %I IEEE Computer Society %K Gaussian KLD, Kullback-Leibler analytical classification classification, coefficient correlation correlation, covariance covariation distance, distribution, estimation, expression, extraction, feature image image, images, invariant lower-order matrices matrices, measurement, optimal random real realistic retrieval, rotation, similarity steerable subGaussian subband technique, texture texture, theory, transform, transforms vector, wavelet %P 397--401 %R 10.1109/ACSSC.2004.1399161 %T SubGaussian rotation-invariant features for steerable wavelet-based image retrieval %V 1 %X This paper presents a new rotation-invariant image retrieval method, which extends a recently introduced classification technique based on steerable wavelet transforms. In the proposed procedure, the feature extraction step consists of estimating the covariations (lower-order cross-correlations) between the wavelet subband coefficients, which are modeled as subGaussian random vectors. The similarity measurement is carried out first by employing norms calculating the distance between the covariation matrices representing two distinct images and second by evaluating the Kullback-Leibler Distance (KLD) between their corresponding subGaussian distributions. We provide analytical expressions relating the subGaussian features corresponding to a rotated image from the features of the original image. Finally, we relate the employed optimal lower-order correlation (p/spl les/2) to the degree of nonGaussianity of the wavelet coefficients, and we demonstrate the effectiveness of our method using real texture images.

@inproceedings{Tzagkarakis2004, abstract = {This paper presents a new rotation-invariant image retrieval method, which extends a recently introduced classification technique based on steerable wavelet transforms. In the proposed procedure, the feature extraction step consists of estimating the covariations (lower-order cross-correlations) between the wavelet subband coefficients, which are modeled as subGaussian random vectors. The similarity measurement is carried out first by employing norms calculating the distance between the covariation matrices representing two distinct images and second by evaluating the Kullback-Leibler Distance (KLD) between their corresponding subGaussian distributions. We provide analytical expressions relating the subGaussian features corresponding to a rotated image from the features of the original image. Finally, we relate the employed optimal lower-order correlation (p/spl les/2) to the degree of nonGaussianity of the wavelet coefficients, and we demonstrate the effectiveness of our method using real texture images.}, added-at = {2011-03-27T19:35:34.000+0200}, author = {Tzagkarakis, G. and Beferull-Lozano, B. and Tsakalides, P.}, biburl = {https://www.bibsonomy.org/bibtex/283c5c69f6297b89ca7f5a88f019c1468/cocus}, bookitleaddon = {Novemeber 7--10, 2004}, booktitle = {Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on}, doi = {10.1109/ACSSC.2004.1399161}, file = {:./01399161.pdf:PDF}, interhash = {a2ebd4b2eea24ee054db4b926358b8de}, intrahash = {83c5c69f6297b89ca7f5a88f019c1468}, keywords = {Gaussian KLD, Kullback-Leibler analytical classification classification, coefficient correlation correlation, covariance covariation distance, distribution, estimation, expression, extraction, feature image image, images, invariant lower-order matrices matrices, measurement, optimal random real realistic retrieval, rotation, similarity steerable subGaussian subband technique, texture texture, theory, transform, transforms vector, wavelet}, location = {#ieeeaddr#}, pages = { 397--401}, publisher = {{IEEE} Computer Society}, timestamp = {2011-03-27T19:35:44.000+0200}, title = {SubGaussian rotation-invariant features for steerable wavelet-based image retrieval}, venue = {Pacific Grove, CA, USA}, volume = 1, year = 2004 }