Li, J. and Li, Xuelong and Tao, D. (2008) KPCA for semantic object extraction in images. Pattern Recognition 41 (10), pp. 3244-3250. ISSN 0031-3203.Full text not available from this repository.
In this paper, we kernelize conventional clustering algorithms from a novel point of view. Based on the fully mathematical proof, we first demonstrate that kernel KMeans (KKMeans) is equivalent to kernel principal component analysis (KPCA) prior to the conventional KMeans algorithm. By using KPCA as a preprocessing step, we also generalize Gaussian mixture model (GMM) to its kernel version, the kernel GMM (KGMM). Consequently, conventional clustering algorithms can be easily kernelized in the linear feature space instead of a nonlinear one. To evaluate the newly established KKMeans and KGMM algorithms, we utilized them to the problem of semantic object extraction (segmentation) of color images. Based on a series of experiments carried out on a set of color images, we indicate that both KKMeans and KGMM can offer more elaborate output than the conventional KMeans and GMM, respectively.
|Keyword(s) / Subject(s):||Segmentation, KPCA, KMeans, Kernel KMeans, GMM, Kernel GMM|
|School or Research Centre:||Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems|
|Date Deposited:||07 Feb 2011 13:40|
|Last Modified:||11 Oct 2016 15:27|
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