Spectral graph theoretic methods have recently shown great promise for the problem of image segmentation, but due to the computational demands, applications of such methods to spatiotemporal data have been slow to appear. For even a short video sequence, the set of all pairwise voxel similarities is a huge quantity of data: one second of a 256 ? 384 sequence captured at 30Hz entails on the order of 10¹³ pairwise similarities. 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 spatiotemporal grouping problems. Our approach is based on a technique for the numerical solution of eigenfunction problems known as the Nystr?m method. This method allows extrapolation of the complete grouping solution using only a small number of "typical" samples. In doing so, we successfully exploit the fact that there are far fewer coherent groups in an image sequence than pixels.