Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms (original) (raw)
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2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
This paper deals with texture modeling for classification or retrieval systems using multivariate statistical features. We propose to model neighborhoods of wavelet coefficients using Spherically Invariant Random Vectors (SIRVs). Under this multivariate model we provide a closed form of Kullback-Leibler divergence between joint distributions to measure similarity. The performances of the proposed model in retrieval are conducted on the VisTex image database aiming to compare the recognition rates with conventional approach of using univariate models such as the Generalized Gaussian distribution and with a recent multivariate model of wavelet coefficients called Multivariate Bessel K forms (MBKF).
2009 16th Ieee International Conference on Image Processing, Vols 1-6, 2009
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2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
This paper deals with texture modeling for classification or retrieval systems using multivariate statistical features. The proposed features are defined by the hyperparameters of a copula-based multivariate distribution characterizing the coefficients provided by image decomposition in scale and orientation. As it belongs to the multivariate stochastic models, the copulas are useful to describe pairwise non-linear association in the case of multivariate non-Gaussian density. In this paper, we propose the d-variate Gaussian copula associated to univariate Gamma densities for modeling the texture. Experiments were conducted on the VisTex database aiming to compare the recognition rates of the proposed model with the univariate generalized Gaussian model, the univariate Gamma model, and the generalized Gaussian copula-based multivariate model.
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This paper deals with the joint modeling of color textures in the context of Content Based Image Retrieval (CBIR). We propose a generic multivariate model based on the Generalized Gamma distribution to describe the marginal behavior of texture wavelet detail subbands. Then the information of dependence across color components is incorporated in the modeling process using the Gaussian copula. The multivariate Generalized Gamma distribution (D MG) is advantageous in term of flexibility when compared with other joint models. As similarity measure, we propose a closed-form of the Kullback-leibler (KL) divergence between two Ds MG. Performances of the CBIR system show the superiority of the proposed model over a variety of multivariate models.
IEEE Access, 2019
This paper presents a new statistical model for texture retrieval in the complex wavelet domain. For this purpose, a finite mixture of Weibull distributions (MoWbl) is proposed to characterize the statistical distribution of magnitudes of complex wavelet coefficients. Despite the ability of the mixture model on capturing a wide range of distribution shapes, choosing an appropriate number of mixture components is a challenging task. To this end, we adopt an unsupervised learning of the model parameters based on the Figueiredo-Jain algorithm and maximum-likelihood estimates. As found in all retrieval statistical-based frameworks, the presence of a similarity measure is trivial. Generally, the failure of a retrieval mixture based system is closely related to the choice of the similarity measure that relies mainly on approximations of some divergences and distances. To overcome this limitation, we propose a canonical form of Weibull distribution which allows us to develop an analytic expression of Cauchy-Schwarz divergence (CSD) for MoWbl distributions. Experiments, conducted on three popular datasets, show that the proposed model yields better performance in terms of goodness-of-fit, retrieval, and execution time compared to some related statistical models for texture retrieval.
2012 19th IEEE International Conference on Image Processing, 2012
This paper presents a new multivariate elliptical distribution, namely the multivariate generalized Gamma times an Uniform (MGΓU) distribution. Because it generalizes the multivariate generalized Gaussian distribution (MGGD), the MGΓU distribution is able to fit a wider range of signals. For the bivariate case, we provide a closed-form of the Kullback-Leibler divergence (KLD). We propose the MGΓU distribution for modeling chrominance wavelet coefficients and exercise it in a texture retrieval experiment. A comparative study between some multivariate models on the VisTex and Outex image database is conducted and reveals that the use of the MGΓU distribution of chromiance wavelet coefficient allows an indexing gain compared to other classical approaches such as MGGD and Copula based model).
Color Texture Classification Using Rao Distance between Multivariate Copula Based Models
Lecture Notes in Computer Science, 2011
This paper presents a new similarity measure based on Rao distance for color texture classification or retrieval. Textures are characterized by a joint model of complex wavelet coefficients. This model is based on a Gaussian Copula in order to consider the dependency between color components. Then, a closed form of Rao distance is computed to measure the difference between two Gaussian Copula based probabilty density functions on the corresponding manifold. Results in term of classification rates, show the effectiveness of the Rao geodesic distance when applied on the manifold of Gaussian Copula based probability distributions, in comparison with the Kullback-Leibler divergence.
Statistical wavelet subband modelling for texture classification
Proceedings 2001 International Conference on Image Processing, 2001
Simple wavelet and wavelet packet transforms have often been used for texture characterisation through the analysis of spatial-frequenc y content. However, most previous methods make no use of any statistical analysis of the transforms' subbands. A novel method is now presented for modelling the multivariate distributions of subband coefficients by considering spatially related coefficients. The Bhattacharya and divergence metrics are then used to produce an improved texture classification method for the application to content based image retrieval.
2008 15th IEEE International Conference on Image Processing, 2008
This contribution concerns the retrieval of colour texture. The interband correlation structure is considered by modeling the heavy-tailed image wavelet histograms with a multivariate generalized Gaussian. As a similarity measure we propose to use the Rao geodesic distance, which, in contrast to the Kullback-Leibler divergence, exists in a closed form for any fixed value of the shape parameter of the distribution. We apply this in several retrieval experiments. The modeling of the interband correlation significantly increases retrieval rates, while the geodesic distance is shown to outperform the Kullback-Leibler divergence. A multivariate Laplace distribution yields better results than a Gaussian, indicating the potential of a model with variable shape parameter together with the geodesic distance.