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In this paper, we study and evaluate the application to image segmentation of a p-Laplacian based relaxation of the Cheeger Cut problem. Based on a l1 relaxation of the initial clustering problem, we show that these methods can outperform usual well-known graph based approaches, e.g., min-cut/max-flow algorithm or l2 spectral clustering, for unsupervised and very weakly supervised image segmentation...
Laplacian embedding provides a low dimensional representation for a matrix of pairwise similarity data using the eigenvectors of the Laplacian matrix. The true power of Laplacian embedding is that it provides an approximation of the ratio cut clustering. However, ratio cut clustering requires the solution to be nonnegative. In this paper, we propose a new approach, nonnegative Laplacian embedding,...
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