Unsupervised image segmentation using contourlet domain hidden markov trees model

Authors:
Yuheng Sha;Lin Cong;Qiang Sun;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing and National Key Lab for Radar, Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Lab for Radar, Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Lab for Radar, Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Lab for Radar, Signal Processing, Xidian University, Xi'an, China
Venue:
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Year:
2005

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Abstract

A novel method of unsupervised imagesegmentation using contourlet domain hidden markov trees model is presented. Fuzzy C-mean clustering algorithm is used to capture the likelihood disparity of different texture features. A new context based fusion model is given for preserve more interscale information in contourlet domain. The simulation results of synthetic mosaics and real images show that the proposed unsupervised segmentation algorithm represents a better performance in edge detection and protection and its error probability of the synthetic mosaics is lower than wavelet domain HMT based method.