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Randomized algorithm of spectral clustering and image/video segmentation using a minority of pixels

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2 Author(s)
Sakai, T. ; Inst. of Media & Inf. Technol., Chiba Univ., Chiba, Japan ; Imiya, A.

We propose a randomized algorithm of spectral clustering and apply it to appearance-based image/video segmentation. Spectral clustering is a kernel-based method of grouping data on separate nonlinear manifolds. However, its high computational expensive restricts the applications. Our algorithm exploits random projection and subsampling techniques for reducing dimensionality and cardinality of data. The computation time can be independent of data dimensionality in appearance-based methods, and is quasilinear with respect to the data cardinality. We demonstrate our spectral clustering algorithm in image and video shot segmentation.

Published in:

Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on

Date of Conference:

Sept. 27 2009-Oct. 4 2009