Spectral Curvature Clustering (SCC)

Guangliang Chen, Gilad Lerman

Research output: Contribution to journalArticlepeer-review

278 Scopus citations

Abstract

This paper presents novel techniques for improving the performance of a multi-way spectral clustering framework (Govindu in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, pp. 1150-1157, 2005; Chen and Lerman, 2007, preprint in the supplementary webpage) for segmenting affine subspaces. Specifically, it suggests an iterative sampling procedure to improve the uniform sampling strategy, an automatic scheme of inferring the tuning parameter from the data, a precise initialization procedure for K-means, as well as a simple strategy for isolating outliers. The resulting algorithm, Spectral Curvature Clustering (SCC), requires only linear storage and takes linear running time in the size of the data. It is supported by theory which both justifies its successful performance and guides our practical choices. We compare it with other existing methods on a few artificial instances of affine subspaces. Application of the algorithm to several real-world problems is also discussed.

Original languageEnglish (US)
Pages (from-to)317-330
Number of pages14
JournalInternational Journal of Computer Vision
Volume81
Issue number3
DOIs
StatePublished - Mar 2009

Keywords

  • Face clustering
  • Hybrid linear modeling
  • Iterative sampling
  • Motion segmentation
  • Multi-way spectral clustering
  • Polar curvature

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