Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms
Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms
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DOI：
10.1109/tip.2014.2313232
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发表时间：
2014-03
影响因子：
10.6
通讯作者：
Noureddine Lasmar;Y. Berthoumieu
中科院分区：
文献类型：
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作者：
Noureddine Lasmar;Y. Berthoumieu
在纹理图像检索的框架下，提出了一种新的基于高斯Copula和小波分解的随机多元建模方法。我们利用了copula范式，这使得依赖性结构与边际行为分离成为可能。我们分别使用广义高斯密度和威布尔密度引入了两个新的多元模型。这些模型捕获了子带边缘分布和小波系数之间的相关性。作为一种相似性度量，我们推导了基于高斯公式的多元模型之间杰弗里散度的封闭形式表达式。在知名数据库上的实验结果表明，与最知名的最先进的方法相比，所提出的方法在检索率上有显著提高。
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 dependence 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 with the best known state-of-the-art approaches.