小波域Copula多维模型纹理检索

Texture retrieval using copula multidimensional model on wavelet domain

提出一种有效的小波域copula多维模型的纹理检索方法.针对小波域上各个子带间独立性建模的不足,方法利用小波分解系数的相关结构设计了树状依赖结构,并在这种依赖结构上实现了copula多维分布模型.树状依赖结构能同时捕获尺度间依赖和邻域依赖,且与邻域依赖结构相比该树状结构具有维数低、需要的copula模型个数少的特点.由于copula多维模型较为复杂,很难计算其Kullback-Leibler距离(KLD),本文提出一种基于copula多维模型的KLD相似度检索方法:Copula模型的KLD由其边缘分布函数的KLD和copula函数的KLD组成.在Vis Tex与Brodatz数据库上的实验表明,本文提出的树状依赖结构和相似度检索方法在小波域相关性建模方面计算效率高,较大地提高了纹理图像的检索率,并且能很好地推广到其他小波域(比如复数小波域、方向小波域等).

In this paper, an effective texture retrieval method using copula multidimensional model on wavelet domain is proposed. For make up for the shortcomings of the single statistical model on wavelet subband, a tree- dependence structure on wavelet domain is constructed, and copula multidimensional distribution is implemented on the tree-dependence structure of wavelet domain. Tree-dependence structure can capture both the dependences of inter-scale and neighbor-dependence structures, and it has fewer dimension and fewer number of copula models comparing to the neighbor-dependence structure. Because of the complexity of the copula multidimensional model, it is difficult to deduce the Kullback-Leibler distance （KLD） of copula model. This paper puts forward a kind of retrieval method based on KLD of copula multidimensional model, which is consisted of two components： The KLD of marginal distributions and the KLD of copula function. The experimental results on VisTex and Brodatz databases show that the proposed retrieval method has low computational complexity and high retrieval accuracy, and it is more effective than the state-of-the-art copula methods on wavelet domain. The proposed texture retrieval method can be extended to other wavelet domains such as complex wavelet and directional wavelet domains.