Spectral clustering ensemble for image segmentation

Authors:
Xiuli Ma;Wanggen Wan;Licheng Jiao
Affiliations:
Shanghai University, Shanghai, China;Shanghai University, Shanghai, China;Xidian University, Xi'an, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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Abstract

To make full use of information included in a dataset, a multiway spectral clustering algorithm with joint model is applied to image segmentation. To overcome the sensitivity of the joint model-based multiway spectral clustering to kernel parameter and to produce the robust and stable segmentation results, spectral clustering ensemble algorithm is proposed in this paper, which can make full use of the built-in randomness of spectral clustering and the inaccuracy of Nystrom approximation to produce diversity. Experiments on UCI dataset, textural and SAR images show that, after cluster ensemble, the new algorithm is not only more robust but also better quality. Therefore, the new algorithm is effective.