Mean shift: A robust approach toward feature space analysis (2002)
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| Venue: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Citations: | 936 - 33 self |
BibTeX
@ARTICLE{Comaniciu02meanshift:,
author = {Dorin Comaniciu and Peter Meer},
title = {Mean shift: A robust approach toward feature space analysis},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2002},
volume = {24},
pages = {603--619}
}
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Abstract
Abstract A general nonparametric technique is proposed for the analysis of a complex multimodal featurespace 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 ofa 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 procedureto 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 andimage 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. Extensiveexperimental results illustrate their excellent performance.
