doi: 10.7763/IJCTE.2015.V7.967
Texture Segmentation Using Graph Cuts in Spectral Decomposition Based Riemannian Multi-Scale Nonlinear Structure Tensor Space
Shoudong Han1
,
Xinyu Wang
- 1National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
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.
Keywords
- Multi-scale nonlinear structure tensor (MSNST)
- graph cuts
- texture segmentation
How to 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. https://doi.org/10.7763/IJCTE.2015.V7.967
Copyright & License
Copyright © 2015 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).