IEEE Trans Image Process. 2015 Aug;24(8):2344-54. doi: 10.1109/TIP.2015.2422575. Epub 2015 Apr 13.
Obtaining robust and efficient rotation-invariant texture features in content-based image retrieval field is a challenging work. We propose three efficient rotation-invariant methods for texture image retrieval using copula model based in the domains of Gabor wavelet (GW) and circularly symmetric GW (CSGW). The proposed copula models use copula function to capture the scale dependence of GW/CSGW for improving the retrieval performance. It is well known that the Kullback-Leibler distance (KLD) is the commonly used similarity measurement between probability models. However, it is difficult to deduce the closed-form of KLD between two copula models due to the complexity of the copula model. We also put forward a kind of retrieval scheme using the KLDs of marginal distributions and the KLD of copula function to calculate the KLD of copula model. The proposed texture retrieval method has low computational complexity and high retrieval precision. The experimental results on VisTex and Brodatz data sets show that the proposed retrieval method is more effective compared with the state-of-the-art methods.
在基于内容的图像检索领域中，获取稳健且高效的旋转不变纹理特征是一项具有挑战性的工作。我们提出了三种使用基于广义高斯（GW）和圆对称广义高斯（CSGW）的 Copula 模型的有效旋转不变纹理图像检索方法。所提出的 Copula 模型使用 Copula 函数来捕获 GW/CSGW 的尺度依赖性，以提高检索性能。众所周知，Kullback-Leibler 距离（KLD）是概率模型之间常用的相似性度量。然而，由于 Copula 模型的复杂性，很难推导出两个 Copula 模型之间的 KLD 的闭式解。我们还提出了一种使用边缘分布的 KLD 和 Copula 函数的 KLD 来计算 Copula 模型的 KLD 的检索方案。所提出的纹理检索方法具有低计算复杂度和高检索精度。在 VisTex 和 Brodatz 数据集上的实验结果表明，与现有方法相比，所提出的检索方法更为有效。
