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Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis

  • Tomoya Sakai
  • Atsushi Imiya
Part of the Advances in Pattern Recognition book series (ACVPR)

Abstract

This chapter presents some practical algorithms of spectral clustering for large-scale data. Spectral clustering is a kernel-based method of grouping data on separate nonlinear manifolds. Reducing its computational expense without critical loss of accuracy contributes to its practical use especially in vision-based applications. The present algorithms exploit random projection and subsampling techniques for reducing dimensionality and the cost for evaluating pairwise similarities of data. The computation time is quasilinear with respect to the data cardinality, and it can be independent of data dimensionality in some appearance-based applications. The efficiency of the algorithms is demonstrated in appearance-based image/video segmentation.

Keywords

Singular Value Decomposition Random Matrix Singular Vector Spectral Cluster Normalize Mutual Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

The first author was partially supported by the Grant-in-Aid for Young Scientists, from the Ministry of Education, Culture, Sports, Science and Technology of Japan under MEXT KAKEN 22700163.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Nagasaki UniversityNagasakiJapan

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