带方向特征的Contourlet HMT模型

根据Shannon信息理论将纹理的方向特征定义为:图像中信号取值为随机分布的奇异值时方向变量的取值特征.根据这一定义,并结合Tamura方向特征的求取方法,文中对Contourlet变换系数的方向概率分布进行了研究,获得方向特征在Contourlet变换的父子子带间形成传递这一结论,在此基础上结合Contourlet隐Markov树(HMT)模型,建立了以隐状态变量分布为条件的方向隐变量的概率分布模型,即带方向特征的Contourlet HMT模型,给出了该模型的结构和训练方法.此外,通过基于所提出模型的无监督结合上下文信息的图像分割算法对合成图像和遥感图像的目标分割实验验证了所提出模型的有效性.

Contourlet HMT model with directional feature

According to Shannon＇s information theory,the directional feature of texture is de-ned as the value of directional variable when an image signal attains a singularity of random distribution.In terms of this de-nition,we calculate the texture＇s directional features using Tamura＇s method and study the directional probability distribution of Contourlet coe-cients.Then we-nd that the directional features tend to be conveyed across parent and child subbands.Based on this conclusion,we establish a novel probability distribution model of hidden direction variables under the condition of hidden state variable＇s distribution,named Contourlet HMT model with directional feature.The structure and training method of the model are presented as well.Moreover,an unsupervised context-based image segmentation algorithm is proposed on the basis of the proposed model.Its e-ectiveness is veri-ed via extensive experiments carried out on several synthesized images and remote sensing images.