spectral clustering ensemble for image segmentation
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.
spectral clustering cluster ensemble image segmentation. nystrom approximation