论文标题
基于剪切域中的copula多元建模的颜色纹理图像检索
Color Texture Image Retrieval Based on Copula Multivariate Modeling in the Shearlet Domain
论文作者
论文摘要
在本文中，基于使用Copula多元模型的剪切域建模提出了颜色纹理图像检索框架。在提出的框架中，高斯副群用于建模非子样品剪切转换（NSST）的不同子带之间的依赖性，而非高斯模型用于系数的边际建模。提出了六种不同的方案，以根据定义的四种类型的相邻类型对NSST系数进行建模；此外，在两种高斯副物和非高斯函数的不同情况下计算了Kullback Leibler Divergence（KLD）关闭形式，以便研究拟议的检索框架中的相似性。 Jeffery Divergence（JD）标准是KLD的对称版本，用于研究所提出的框架中的相似性。我们已经对四个纹理图像检索基准数据集实施了实验，其结果表明了所提出的框架优于现有的最新方法。此外，还在特征提取和相似性匹配的两个步骤中分析了提议框架的检索时间，这也表明所提出的框架享有适当的检索时间。
In this paper, a color texture image retrieval framework is proposed based on Shearlet domain modeling using Copula multivariate model. In the proposed framework, Gaussian Copula is used to model the dependencies between different sub-bands of the Non Subsample Shearlet Transform (NSST) and non-Gaussian models are used for marginal modeling of the coefficients. Six different schemes are proposed for modeling NSST coefficients based on the four types of neighboring defined; moreover, Kullback Leibler Divergence(KLD) close form is calculated in different situations for the two Gaussian Copula and non Gaussian functions in order to investigate the similarities in the proposed retrieval framework. The Jeffery divergence (JD) criterion, which is a symmetrical version of KLD, is used for investigating similarities in the proposed framework. We have implemented our experiments on four texture image retrieval benchmark datasets, the results of which show the superiority of the proposed framework over the existing state-of-the-art methods. In addition, the retrieval time of the proposed framework is also analyzed in the two steps of feature extraction and similarity matching, which also shows that the proposed framework enjoys an appropriate retrieval time.
