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Support vector machines for histogram-based image classification
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Incremental Nonlinear Dimensionality Reduction by Manifold Learning
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Graph Partitioning Active Contours (GPAC) for Image Segmentation
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Data Fusion and Multicue Data Matching by Diffusion Maps
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On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
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| Hi-index | 0.16 |
Abstract--Spectral graph theoretic methods have recently shown great promise for the problem of image segmentation. However, due to the computational demands of these approaches, applications to large problems such as spatiotemporal data and high resolution imagery have been slow to appear. The contribution of this paper is a method that substantially reduces the computational requirements of grouping algorithms based on spectral partitioning making it feasible to apply them to very large grouping problems. Our approach is based on a technique for the numerical solution of eigenfunction problems known as the Nyström method. This method allows one to extrapolate the complete grouping solution using only a small number of samples. In doing so, we leverage the fact that there are far fewer coherent groups in a scene than pixels.