1 July 2010 Unsupervised texture image segmentation using multilayer data condensation spectral clustering
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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.
© (2010) Society of Photo-Optical Instrumentation Engineers (SPIE)
Hanqiang Liu, Hanqiang Liu, Licheng Jiao, Licheng Jiao, Feng Zhao, Feng Zhao, } "Unsupervised texture image segmentation using multilayer data condensation spectral clustering," Journal of Electronic Imaging 19(3), 031203 (1 July 2010). https://doi.org/10.1117/1.3455990 . Submission:
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