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Volume: 4 | Article ID: art00106
Kernel Based Spectral Image Segmentation
  DOI :  10.2352/CGIV.2008.4.1.art00106  Published OnlineJanuary 2008

In this work, we propose a new algorithm for spectral image segmentation based on the use of a kernel matrix. An efficient multiscale method is presented for accelerating spectral image segmentation. The multiscale strategy uses the lattice geometry of images to construct an image pyramid whose hierarchy provides a framework for rapidly estimating eigenvectors of normalized kernel matrices. To prevent the boundaries from deteriorating, the image size on the top level of the pyramid is generally required to be around 75×75, where the eigenvectors of normalized kernel matrices would be approximately solved by the Nyström method. Within this hierarchical structure, the coarse solution is increasingly propagated to finer levels and is refined using subspace iteration. Experimental results have shown that the proposed method can perform significantly well in spectral image segmentation as well as speed up the approximation of the eigenvectors of normalized kernel matrices.

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Hongyu Li, Vladimir Bochko, Timo Jaaskelainen, Jussi Parkkinen, I-Fan Shen, "Kernel Based Spectral Image Segmentationin Proc. IS&T CGIV 2008/MCS'08 4th European Conf. on Colour in Graphics, Imaging, and Vision 10th Int'l Symp. on Multispectral Colour Science,  2008,  pp 494 - 498,

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