Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
