Spectral grouping using the Nystrom method Export

@article{citeulike:2325478, 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 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.}, author = {Fowlkes, C. and Belongie, S. and Chung, F. and Malik, J.}, booktitle = {Transactions on Pattern Analysis and Machine Intelligence}, citeulike-article-id = {2325478}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TPAMI.2004.1262185}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1262185}, doi = {10.1109/TPAMI.2004.1262185}, journal = {Transactions on Pattern Analysis and Machine Intelligence}, keywords = {large-scale, spectral-clustering}, number = {2}, pages = {214--225}, posted-at = {2008-02-03 11:44:27}, priority = {0}, title = {Spectral grouping using the Nystrom method}, url = {http://dx.doi.org/10.1109/TPAMI.2004.1262185}, volume = {26}, year = {2004} }

TY - JOUR ID - citeulike:2325478 L3 - citeulike-article-id:2325478 N2 - 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. IS - 2 JF - Transactions on Pattern Analysis and Machine Intelligence EP - 225 TI - Spectral grouping using the Nystrom method VL - 26 T2 - Transactions on Pattern Analysis and Machine Intelligence SP - 214 KW - large-scale KW - spectral-clustering AU - Fowlkes, C AU - Belongie, S AU - Chung, F AU - Malik, J PY - 2004/// UR - http://dx.doi.org/10.1109/TPAMI.2004.1262185 ER -