A finite mixture of Weibull based statistical model for texture retrieval in the complex wavelet domain (original) (raw)
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A joint model of complex wavelet coefficients for texture retrieval
2009
We present a Copula-based statistical model of complex wavelet coefficient magnitudes for color texture image retrieval. Our model is based on two-parameter Weibull distributions and a multivariate Student t Copula. For similarity measurement we employ a Monte- Carlo approach to approximate the Kullback-Leibler divergence between two models. The experimental retrieval results show that the incorporation of the dependency structure between subbands significantly improves retrieval accuracy compared to previous approaches.
2008
In this work, we present a texture-image retrieval approach, which is based on the idea of measuring the Kullback-Leibler divergence between the marginal distributions of complex wavelet coefficient magnitudes. We employ Kingsbury's Dual-Tree Complex Wavelet Transform for image decomposition and propose to model the detail subband coefficient magnitudes by either two-parameter Weibull or Gamma distributions for which we provide closed-form solutions to the Kullback-Leibler divergence. The experimental results indicate that our approach can achieve higher retrieval rates than the classical approach of using the pyramidal Discrete Wavelet Transform together with the Generalized Gaussian model for detail subband coefficients.
2010
This paper presents a texture descriptor based on the fine detail coefficients at three resolution levels of a traslation invariant undecimated wavelet transform. First, we consider vertical and horizontal wavelet detail coefficients at the same position as the components of a bivariate random vector, and the magnitude and angle of these vectors are computed. The magnitudes are modeled by a Generalized Gamma distribution. Their parameters, together with the circular histograms of angles, are used to characterize each texture image of the database. The Kullback-Leibler divergence is used as the similarity measurement. Retrieval experiments, in which we compare two wavelet transforms, are carried out on the Brodatz texture collection. Results reveal the good performance of this wavelet-based texture descriptor obtained via the Generalized Gamma distribution.
This paper presents a texture descriptor based on wavelet frame transforms. At each position in the image, and for each resolution level, we consider both vertical and horizontal wavelet detail coefficients as the components of a bivariate random vector. The magnitudes and angles of these vectors are computed. At each level the empirical histogram of magnitudes is modeled by a Generalized Gamma distribution , and the empirical histogram of angles is modeled by a different version of the von Mises distribution that accounts for histograms with 2 modes. Each texture is characterized by few parameters. A new distance is presented (based on the Kullback-Leibler divergence) that allows giving relative importance to each model and to each resolution level. This distance is later conveniently adapted to provide for rotation invariance, by establishing equivalence classes over distributions of angles. Through a broad set of experiments on three different image databases, we demonstrate that our new descriptor and distance measure can be successfully applied in the context of texture retrieval. We compare our system to several relevant methods in this field in terms of retrieval performance and number of parameters used by each method. We also include some classification tests. In all the tests, we obtain superior retrieval rates for a set of fewer parameters involved.
2009 16th Ieee International Conference on Image Processing, Vols 1-6, 2009
We study the retrieval of coloured textures from a database. In a statistical framework we model the heavy-tailed wavelet histograms through a generalized Gaussian distribution (GGD). We choose the Kullback-Leibler divergence (KLD) as a similarity measure and we obtain a closed-form expression for the KLD between two zero-mean bivariate GGDs. This allows us to take into account the rich correlation structure between the colour bands two by two. We show that this results in a considerably improved retrieval rate and, in addition, we demonstrate the superior performance of the bivariate GGD, in comparison with the bivariate Gaussian.
Multivariate statistical modeling for texture analysis using wavelet transforms
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).
Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms
IEEE Transactions on Image Processing, 2014
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.
A wavelet based image retrieval
Pattern Recognition and Machine Intelligence, 2005
In this report, we propose a wavelet-based content descriptor with which we implement an image retrieval system. Initially, we propose the wavelet-based weighted standard deviation texture descriptor. We then show how to extend this descriptor to characterize both texture and color in images. Thus, we obtain a compact feature vector that characterizes images in terms of both texture and color. We use this feature vector to implement an image retrieval system, using the weighted L1 distance measure.
Wavelet-Based Texture Retrieval using Independent Component Analysis
2007 IEEE International Conference on Image Processing, 2007
In this paper, a novel approach to texture retrieval using independent component analysis (ICA) in wavelet domain is proposed. It is well recognized that the wavelet coefficients in different subbands are statistically correlated, resulting in the fact that the product of the marginal distributions of wavelet coefficients is not accurate enough to characterize the stochastic properties of texture images. To tackle this problem, we employ (ICA) in feature extraction to decorrelate the analysis coefficients in different subbands, followed by modeling the marginal distributions of the separated sources using generalized Gaussian density (GGD), and perform similarity measure based on the maximum likelihood criterion. It is demonstrated by simulation results on a database consisting of 1776 texture images that the proposed method improve the accuracy of texture image retrieval in terms of average retrieval rate, compared with the traditional method using GGD for feature extraction and Kullback-Leibler divergence for similarity measure.
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.