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 Nystrom 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.
- [FBC+04]
- Fowlkes, C.C., S. Belongie, F. Chung and J. Malik. Spectral grouping using the nystrom method. IEEE PAMI, 26(2):214-225, 2004.
Computational Vision at UC Irvine