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General Information
Editor-in-chief
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2015 Vol.7(4): 259-263 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2015.V7.967

Texture Segmentation Using Graph Cuts in Spectral Decomposition Based Riemannian Multi-Scale Nonlinear Structure Tensor Space

Shoudong Han and Xinyu Wang
Abstract—This paper proposes an interactive texture segmentation method base on graph cuts. It extracts the texture features by using multi-scale nonlinear structure tensor, and discusses dissimilarity measure and probability distribution of features in the Riemannian space, which are used to design the edge-based and region-based items of the segmentation model respectively. To construct distributions, we employ the Gaussian mixture model with covariant-scale based full covariance structure. Additionally, we propose the spectral decomposition based recursive clustering algorithm to estimate the corresponding statistics. The comparisons of various texture segmentation experiments demonstrate the validity of the proposed method.

Index Terms—Multi-scale nonlinear structure tensor (MSNST), graph cuts, texture segmentation.

S. D. Han is with the National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: shoudonghan@hust.edu.cn).
X. Y. Wang is with the Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: wxyhust@163.com).

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Cite:Shoudong Han and Xinyu Wang, "Texture Segmentation Using Graph Cuts in Spectral Decomposition Based Riemannian Multi-Scale Nonlinear Structure Tensor Space," International Journal of Computer Theory and Engineering vol. 7, no. 4, pp. 259-263, 2015.

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