Mean-shift
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Mean-shift is a non-parametric feature space analysis technique.[1] Application domains include clustering in computer vision and image processing.
[edit] History
The mean shift procedure was originally presented in 1975 by Fukunaga and Hostetler. [2]
[edit] Overview
Mean shift is a procedure for locating stationary points of a density function given discrete data sampled from that function.[1] It is useful for detecting the modes of this density.[1]
[edit] References
- ^ a b c Comaniciu, Dorin; Peter Meer (May 2002). "Mean Shift: A Robust Approach Toward Feature Space Analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (5): 603–619. IEEE. doi:.
- ^ Fukunaga, Keinosuke; Larry D. Hostetler (January 1975). "The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition". IEEE Transactions on Information Theory 21 (1): 32–40. IEEE.
