Spectral methods
The journal club looked at the following two papers in detail:
- On Spectral Clustering: Analysis and an algorithm. Ng A.Y., Jordan, M.I., and Weiss Y. NIPS 2001.
- A Random Walks View of Spectral Segmentation. Marina Meila and Jianbo Shi. AI and STATISTICS (AISTATS) 2001.
Normalized Cuts and Image Segmentation. IEEE Conf. Computer Vision and Pattern Recognition(CVPR), June 1997. There is a much longer version of this paper from IEEE Transactions on Pattern Analysis and Machine Intelligence 2000.
Here are some other related papers.
"Unifying views"
Segmentation using eigenvectors: a unifying view. Weiss Y. Proceedings IEEE International Conference on Computer Vision p. 975-982 (1999) http://www.cs.huji.ac.il/~yweiss/iccv99.ps.gz http://www.cs.huji.ac.il/~yweiss/iccv99.pdf
Proc. AISTATS 2003 A unifying theorem for spectral embedding and clustering. Matthew Brand, Kun Huang. http://research.microsoft.com/conferences/aistats2003/proceedings/189.ps http://research.microsoft.com/conferences/aistats2003/proceedings/189.pdf A supplementary web page: http://research.microsoft.com/conferences/aistats2003/proceedings/189/189.htm
Energy Functions
What Energy Functions can be Minimized via Graph Cuts? Vladimir Kolmogorov and Ramin Zabih. European Conference on Computer Vision, May 2002. http://www.cs.cornell.edu/rdz/Papers/KZ-ECCV02-graphcuts.pdf Expanded version (under review): http://www.cs.cornell.edu/rdz/Papers/graph_cuts_pami.pdf
Spectral Graph theory
Lectures on Spectral Graph Theory Fan R. K. Chung http://math.ucsd.edu/~fan/cbms.pdf