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.
In this paper, a novel local manifold spectral clustering with fuzzy c-means (FCM) data condensation is presented.
Firstly, a multilayer FCM data condensation method is performed on the original data to contain a condensation subset.
Secondly, the spectral clustering algorithm based on the local manifold distance measure is used to realize the
classification of the condensation subset. Finally, the nearest neighbor method is adopted to obtain the clustering result
of the original data. Compared with the standard spectral clustering algorithm, the novel method is more robust and has
the advantages of effectively dealing with the large scale data. In our experiments, we first analyze the performances of
multilayer FCM data condensation and local manifold distance measure, then apply our method to solve image
segmentation and the large Brodatz texture images classification. The experimental results show that the method is
effective and extensible, and especially the runtime of this method is acceptable.
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