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Journal of Electronic Imaging

Unsupervised texture image segmentation using multilayer data condensation spectral clustering
Author(s): Hanqiang Liu; Licheng Jiao; Feng Zhao
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Paper Abstract

A novel unsupervised texture image segmentation using a multilayer data condensation spectral clustering algorithm is presented. First, the texture features of each image pixel are extracted by the stationary wavelet transform and a multilayer data condensation method is performed on this texture features data set to obtain a condensation subset. Second, the spectral clustering algorithm based on the manifold similarity measure is used to cluster the condensation subset. Finally, according to the clustering result of the condensation subset, the nearest-neighbor method is adopted to obtain the original image-segmentation result. In the experiments, we apply our method to solve the texture and synthetic aperture radar image segmentation and take self-tuning k-nearest-neighbor spectral clustering and Nyström methods for baseline comparisons. The experimental results show that the proposed method is more robust and effective for texture image segmentation.

Paper Details

Date Published: 1 July 2010
PDF: 7 pages
J. Electron. Imag. 19(3) 031203 doi: 10.1117/1.3455990
Published in: Journal of Electronic Imaging Volume 19, Issue 3
Show Author Affiliations
Hanqiang Liu, Xidian Univ. (China)
Licheng Jiao, Xidian Univ. (China)
Feng Zhao, Xidian Univ. (China)

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