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.
