Spectral
Curvature Clustering
(SCC)
Spectral
Curvature
Clustering (SCC) is a multi-way spectral clustering algorithm for
solving the problem
of hybrid
linear modeling, that
is, to model and segment data using an arrangement of affine subspaces.
For the justification of the
algorithm and its underlying theory, please refer to the FoCM paper
below; for the practical techniques and numerical results, please see
the IJCV paper below.
Publications
- Motion Segmentation for Hopkins 155 Database Via SCC [PDF], G. Chen and G.
Lerman, ICCV Workshop on Dynamical Vision, September 2009, Kyoto, Japan.
- Foundations of a
Multi-way Spectral Clustering Framework for Hybrid Linear Modeling [PDF], G. Chen
and G. Lerman, Journal of Foundations of Computational Mathematics,
2009.
- Spectral Curvature
Clustering (SCC) [PDF],
G. Chen and G.
Lerman, International Journal of Computer Vision, 81:317-330, 2009. DOI
10.1007/s11263-008-0178-9.
Talk at
the IMA
Hot Topics Workshop: Multi-Manifold
Data Modeling and Applications
Matlab
Codes
- SCC
(by Guangliang Chen)
- Other algorithms
compared with in the IJCV paper:
C++ Codes of SCC (implemented
by Amit Hooda; have
NOT been tested by the authors)
Supplemental Data
- Artificial data: generated by using 'generate_samples.m' which is
contained in the GPCA-voting
folder
- Real data
- Motion Segmentation (Data,
source: http://www.suri.it.okayama-u.ac.jp/e-program-separate.html)
- Face Clustering (Yale
Face Database B)
- Temporal Partitioning of Video Sequences (Video,
provided
by Rene Vidal)
- Benchmark: Hopkins 155 Database
Extensions
to Multi-Manifold Data Modeling
- Kernel Spectral Curvature Clustering (KSCC) [PDF], G. Chen, S. Atev and
G. Lerman, ICCV Workshop on Dynamical Vision, September 2009, Kyoto,
Japan.
Contact
Info
Acknowledgement
- The research
described here was supported by NSF grant #0612608.
________________________________________
Last updated
on
10/14/2009.