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| Space Feature Based Spectral Clustering for Noisy Image Segmentation |
| LIU Han-Qiang1, ZHAO Feng2 |
1.School of Computer Science,Shaanxi Normal University,Xian 710062
2.School of Telecommunications and Information Engineering,Xian University of Posts and Telecommunications,Xian 710061 |
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Abstract To overcome the problem that the traditional spectral clustering is easily influenced by image noise while applied to noisy image segmentation, a space feature based spectral clustering algorithm for noise image segmentation is proposed. In this method, gray value, local spatial information and non-local spatial information of each pixel are utilized to construct a 3-dimensional feature dataset. Then, the space compactness function is introduced to compute the similarity between each feature point and its K nearest neighbors. Finally, the final image segmentation result is obtained by spectral clustering algorithm. Some noisy artificial images, nature images and synthetic aperture radar images are utilized and normalized. Cut, FCM_s and Nystrom method are compared with the proposed method in the experiments. The experimental results show that the proposed method is robustness and obtains the satisfying segmentation result.
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Received: 16 August 2011
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2012, Vol. 25 
