IEEE Trans Image Process. 2014 May;23(5):2246-61. doi: 10.1109/TIP.2014.2313232. Epub 2014 Mar 24.
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.
在纹理图像检索的框架中，提出了一种基于高斯 Copula 和小波分解的新的随机多元模型族。我们利用 Copula 范例，使我们能够将依赖性结构与边缘行为分开。我们分别使用广义高斯和 Weibull 密度引入了两种新的多元模型。这些模型既捕获了子带边缘分布，又捕获了小波系数之间的相关性。作为相似性度量，我们推导出基于高斯 Copula 的多元模型之间的 Jeffrey 散度的闭式表达式。在著名数据库上的实验结果表明，与最先进的方法相比，使用所提出的方法在检索率方面有了显著的提高。
