Mean shift: A robust approach toward feature space analysis.
Dorin Comaniciu and Peter Meer(*)
Imaging and Visualization Department
Siemens Corporate Research
Princeton, NJ 08540
(*)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08855, USA
A general nonparametric technique is proposed for the analysis of a
complex multimodal feature space and to delineate arbitrarily
shaped clusters in it. The basic computational module of the
technique is
an old pattern recognition procedure, the mean shift. We prove for
discrete data the convergence of a recursive mean shift procedure
to the nearest stationary point of the underlying density function
and thus its utility in detecting the modes of the density.
The equivalence of the mean shift procedure to the
Nadaraya--Watson estimator from kernel regression and the robust
M-estimators of location is also established. Algorithms for two
low-level vision tasks, discontinuity preserving smoothing and
image segmentation are described as applications. In these algorithms
the only user set parameter is the resolution of the analysis, and
either gray level or color images are accepted as input. Extensive
experimental results illustrate their excellent performance.
