Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Labeling and Grouping by Minimizing Linear Functionals over Cones
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Global optimization using embedded graphs
Global optimization using embedded graphs
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We apply a novel optimization technique, semidefinite programming, to the unsupervised partitioning of images. Representing images by graphs which encode pairwise (dis)similarities of local image features, a partition of the image into coherent groups is computed by determining optimal balanced graph cuts. Unlike recent work in the literature, we do not make any assumption concerning the objective criterion like metric pairwise interactions, for example. Moreover, no tuning parameter is necessary to compute the solution. We prove that, from the optimization point of view, our approach cannot perform worse than spectral relaxation approaches which, conversely, may completely fail for the unsupervised choice of the eigenvector threshold.