Rotation-Invariant Texture Retrieval with Gaussianized Steerable Pyramids

Tzagkarakis, George; Beferall-Lozano, B.; Tsakalides, Panagiotis

doi:10.1109/icassp.2005.1415429

Cited by 17 publications

(42 citation statements)

References 39 publications

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“…We note that, although using computationally complex Gaussianization procedure of [20] to obtain Gaussian subbands in order to exploit MG model, the retrieval performance of MG model is less than these of univariate models. We point out that even if MGG model has achieved good performance when it was used with color texture images [32], its discrimination power decreases when it is employed to model only the spatial dependency of wavelet coefficients from grayscale texture images.…”

Section: E Retrieval Performance With Eb1: Other Multivariate Modelsmentioning

confidence: 99%

“…of [27]- [29], multivariate Gaussian (MG) model used by [20] and the Multivariate generalized Gaussian (MGG) model introduced in [32].…”

Section: A Experimental Settingmentioning

confidence: 99%

“…We use the Gaussianization method described in [20] to obtain Gaussianized wavelet subbands and hence we can model wavelet coefficients with MG model. The KLD and as a consequence the JD between MG models are widely used in literature, they can be found for instance in [60].…”

Section: E Retrieval Performance With Eb1: Other Multivariate Modelsmentioning

confidence: 99%

“…For this reason, some authors have started to study the joint statistics of wavelet coefficients via multivariate modeling. Tzagkarakis et al [20] proposed a computationally complex Gaussianization procedure of filter bank outputs in order to model wavelet coefficients by the multivariate Gaussian distribution. Powerful statistical algorithms have been developed for image denoising using a Multivariate generalized Gaussian distribution (MGGD) [21] or Elliptically Contoured Distribution (ECD) [22], but no closed expression exists for the KLD between these joint distributions to measure similarity in a retrieval or classification context.…”

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confidence: 99%

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Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms

Lasmar

Berthoumieu

2014

IEEE Trans. on Image Process.

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Abstract-In the framework of texture image retrieval, a new family of stochastic multivariate modeling is proposed based on Gaussian Copula and wavelet decompositions. We take advantage of the copula paradigm which makes it possible to separate dependency structure from marginal behavior. We introduce two new multivariate models using respectively generalized Gaussian and Weibull densities. These models capture both the subband marginal distributions and the correlation between wavelet coefficients.We derive, as a similarity measure, a closed form expression of the Jeffrey divergence between Gaussian Copula-based multivariate models. Experimental results on well-known databases show significant improvements in retrieval rates using the proposed method compared to the best known state-of-the-art approaches.

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Section: E Retrieval Performance With Eb1: Other Multivariate Modelsmentioning

confidence: 99%

“…of [27]- [29], multivariate Gaussian (MG) model used by [20] and the Multivariate generalized Gaussian (MGG) model introduced in [32].…”

Section: A Experimental Settingmentioning

confidence: 99%

Section: E Retrieval Performance With Eb1: Other Multivariate Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

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Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms

Lasmar

Berthoumieu

2014

IEEE Trans. on Image Process.

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“…The concept is very valuable for image analysis; in particular, local orientation analysis, contour detection, shape from shading [5], texture retrieval [6], [7], and directional pattern detection [8]. The steerable pyramid is also self-reversible, which translates into the fact that the corresponding wavelets form a tight frame of [9].…”

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confidence: 99%

Steerable Pyramids and Tight Wavelet Frames in $L_{2}({\BBR}^{d})$

Unser

Chenouard

Ville

2011

IEEE Trans. on Image Process.

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Abstract-We present a functional framework for the design of tight steerable wavelet frames in any number of dimensions. The 2-D version of the method can be viewed as a generalization of Simoncelli's steerable pyramid that gives access to a larger palette of steerable wavelets via a suitable parametrization. The backbone of our construction is a primal isotropic wavelet frame that provides the multiresolution decomposition of the signal. The steerable wavelets are obtained by applying a one-to-many mapping ( th-order generalized Riesz transform) to the primal ones. The shaping of the steerable wavelets is controlled by an unitary matrix (where is the number of wavelet channels) that can be selected arbitrarily; this allows for a much wider range of solutions than the traditional equiangular configuration (steerable pyramid). We give a complete functional description of these generalized wavelet transforms and derive their steering equations. We describe some concrete examples of transforms, including some built around a Mallat-type multiresolution analysis of , and provide a fast Fourier transform-based decomposition algorithm. We also propose a principal-component-based method for signaladapted wavelet design. Finally, we present some illustrative examples together with a comparison of the denoising performance of various brands of steerable transforms. The results are in favor of an optimized wavelet design (equalized principal component analysis), which consistently performs best.

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Multivariate Texture Discrimination Based on Geodesics to Class Centroids on a Generalized Gaussian Manifold

Shabbir

Verdoolaege

Oost

2013

Lecture Notes in Computer Science

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Abstract.A texture discrimination scheme is proposed wherein probability distributions are deployed on a probabilistic manifold for modeling the wavelet statistics of images. We consider the Rao geodesic distance (GD) to the class centroid for texture discrimination in various classification experiments. We compare the performance of GD to class centroid with the Euclidean distance in a similar context, both in terms of accuracy and computational complexity. Also, we compare our proposed classification scheme with the k-nearest neighbor algorithm. Univariate and multivariate Gaussian and Laplace distributions, as well as generalized Gaussian distributions with variable shape parameter are each evaluated as a statistical model for the wavelet coefficients. The GD to the centroid outperforms the Euclidean distance and yields superior discrimination compared to the k-nearest neighbor approach.

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Rotation-Invariant Texture Retrieval with Gaussianized Steerable Pyramids

Cited by 17 publications

References 39 publications

Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms

Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms

Steerable Pyramids and Tight Wavelet Frames in $L_{2}({\BBR}^{d})$

Multivariate Texture Discrimination Based on Geodesics to Class Centroids on a Generalized Gaussian Manifold

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