Mean shift: a robust approach toward feature space analysis
Comaniciu, D.; Meer, P.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 24, Issue 5, May 2002 Page(s):603 - 619
Digital Object Identifier 10.1109/34.1000236
Summary:A general non-parametric 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. For discrete data, we
prove 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 relation 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
View citation and abstract |
Cart
