基于双树复小波和广义高斯密度的纹理图像检索

提出了一种基于双树复小波变换结合广义高斯密度和Kullback-Leibler距离的纹理图像检索新方法。该方法运用双树复小波变换对检索图像和目标图像进行分解,在每层生成6个方向子带的小波系数,并对小波系数的边缘分布函数进行高斯建模,生成纹理特征,再通过计算相应子带间纹理特征的Kullback-Leibler距离度量图像的相似性。实验表明,该方法比基于能量特征和欧氏距离的检索方法以及在3层分解层数下比基于小波变换、Contourlet变换等结合广义高斯模型的检索方法有更高的检索率。

Texture image retrieval based on DT-CWT and generalized gaussian density

A new texture image retrieval method based on dual tree complex wavelet transform(DT-CWT) and Generalized Gaussian Density was proposed.By using the DT-CWT the query images and target images are decomposed to six directional sub-bands at each level.Modeling the marginal distribution of dual tree complex wavelet coefficients using Generalized Gaussian Density(GGD) to generate texture feature vectors.Kullback-Leibler distance(KLD) function was used as similarity measurement.The experimental results show that this method has higher accuracy than the methods based on energy feature and Euclidean distance as well as methods based on wavelet transform,contourlet transform and others using generalized Gaussian model in the same scale.