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Abstract: Traditional clustering ensemble methods combine all obtained clustering results at hand. However, we can often achieve a better clustering solution if only parts of the clustering results available are combined. In this paper, we generalize the selective clustering ensemble algorithm proposed by Azimi and Fern and a novel clustering ensemble method, SELective Spectral Clustering Ensemble (SELSCE), is proposed. The component clusterings of the ensemble system are generated by spectral clustering (SC) capable of engendering diverse committees. The random scaling parameter, Nyström approximation are used to perturb SC for producing the components of the ensemble system. After the generation of component clusterings, the bagging technique, usually applied in supervised learning, is used to assess the component clustering. We randomly pick part of the available clusterings to get a consensus result and then compute normalized mutual information (NMI) or adjusted rand index (ARI) between the consensus result and the component clusterings. Finally, the components are ranked by aggregating multiple NMI or ARI values. The experimental results on UCI dataset and images demonstrate that the proposed algorithm can achieve a better result than the traditional clustering ensemble methods. [Copyright &y& Elsevier]

Abstract: As an adaptive approximation tool, bandelets exhibit enormous potential in image compression. In this paper, we propose a low complexity adaptive multiscale bandelets transform (AMBT) for synthetic aperture radar (SAR) image compression. Different from optical images, SAR images carry information in low frequency bands as well as high frequency bands. So in our proposed approach multilevel wavelet packet decomposition is performed on images firstly, and then dyadic partition is employed to get some squares on which to perform a bandeletization subsequently. The determination of the optimal direction of geometric flow is one of the most important issues in the implementation of bandelets. Here a cooperative decision of the flow direction is adopted to obtain accurate flows. Because the proposed approach can avoid the bottom to top pruning algorithm of wavelet quadtree and the exhaustive searching of geometric flows in the second generation bandelets, it is of low complexity and thus can be implemented rapidly. Moreover, the proposed AMBT exhibits good performance at low bit rate because of the efficient representation of bandelet in capturing geometrics in image. Finally some experiments were carried out on some SAR images and the results show that our proposed scheme outperforms wavelet and the second generation bandelet methods in both PSNR and time consumption. [Copyright &y& Elsevier]