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Applying neighborhood consistency for fast clustering and kernel density estimation
Kai Zhang; Ming Tang; Kwok, J.T.;
Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
Volume 2,
20-25 June 2005
Page(s):1001
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1007 vol. 2
Abstract:
Nearest neighborhood consistency is an important concept in statistical pattern recognition, which underlies the well-known k-nearest neighbor method. In this paper, we combine this idea with kernel density estimation based clustering, and derive the fast mean shift algorithm (FMS). FMS greatly reduces the complexity of feature space analysis, resulting satisfactory precision of classification. More importantly, we show that with FMS algorithm, we are in fact relying on a conceptually novel approach of density estimation, the fast kernel density estimation (FKDE) for clustering. The FKDE combines smooth and non-smooth estimators and thus inherits advantages from both. Asymptotic analysis reveals the approximation of the FKDE to standard kernel density estimator. Data clustering and image segmentation experiments demonstrate the efficiency of FMS.
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