Abstract—A multiscale texture segmentation approach based
on contextual hidden Markov tree (CHMT) model and boundary
refinement is proposed. A hidden Markov tree (HMT) model is a
probabilistic model for capturing persistence properties of
wavelet coefficients without considering clustering properties.
We have proposed the CHMT model to enhance the clustering
properties by adding extended coefficients associated with
wavelet coefficients in every scale. In this study, we train the
CHMT parameters for every texture and then use them to
compute maximum likelihoods for every dyadic square region at
every scale in an image which will be segmented. Then the
boundary refinement algorithm is adopted to fuse the
different-scale segmented results to improve the final results.
We demonstrate the performance of the proposed method on
synthetic and aerial images; moreover, the comparison with
other methods is also provided to show the effectiveness of the
Index Terms—Contextual hidden Markov tree model, maximum likelihood, multiscale texture image segmentation, wavelet transform.
Din-Chang Tseng and Ruei-Lung Chen are with the Institute of Computer Science and Information Engineering, National Central University, Jhongli, 32001 Taiwan (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Din-Chang Tseng and Ruei-Lung Chen, "Mutiscale Texture Segmentation Using Contextual Hidden Markov Tree Models," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 198-205, 2015.