Contourlet HMT model with directional feature

According to Shanno＇s information theory, the directional feature of texture is defined as the value of directional variable when an image signal attains a singularity of random distribution. In terms of this definition, we calculate the texture＇s directional features using Tamura＇s method and study the directional probability distribution of Contourlet coefficients. Then we find 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 effectiveness is verified via extensive experiments carried out on several synthesized images and remote sensing images.