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2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698) (2003)
Baltimore, MD, USA
July 6, 2003 to July 9, 2003
ISBN: 0-7803-7965-9
pp: 533-536
Jingdong Wang , Dept. of Autom., Tsinghua Univ., Beijing, China
Jianguo Lee , Dept. of Autom., Tsinghua Univ., Beijing, China
Changshui Zhang , Dept. of Autom., Tsinghua Univ., Beijing, China
ABSTRACT
Gaussian mixture model (GMM) is an efficient method for parametric clustering. However, traditional GMM can't perform clustering well on data set with complex structure such as images. In this paper, kernel trick, successfully used by SVM and kernel PCA, is introduced into EM algorithm for solving parameter estimation of GMM, which is so called kernel GMM (kGMM). The basic idea of kernel GMM is to apply kernel based GMM in feature space instead of in input data space. In order to avoid the curse of dimension, the proposed kGMM also embeds a step to automatically select discriminative features in feature space. kGMM is employed for the task of image binarization. Result shows that the proposed approach is feasible.
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CITATION

C. Zhang, J. Lee and J. Wang, "Kernel GMM and its application to image binarization," 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698)(ICME), Baltimore, MD, USA, 2003, pp. 533-536.
doi:10.1109/ICME.2003.1220972
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