Robust Analysis of Feature Spaces:
Color Image Segmentation
Dorin Comaniciu and Peter Meer
Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08855, USA
A general technique for the recovery of significant
image features is presented.
The technique is based on the mean shift
algorithm, a simple nonparametric procedure for estimating
density gradients. Drawbacks of the current methods
(including robust clustering) are avoided.
Feature space of any nature can be processed,
and as an example, color image segmentation is discussed.
The segmentation is completely
autonomous, only its class is chosen by the user.
Thus, the same program can produce
a high quality edge image, or
provide, by extracting all the significant colors,
a preprocessor for content-based query systems.
A 512x512 color image is analyzed in less than 10 seconds
on a standard workstation. Gray level images are handled as
color images having only the lightness coordinate.
Proceedings of
IEEE Conference on Computer Vision and Pattern
Recognition, San Juan, Puerto Rico, June 1997,
750-755.
Return to Research: Robust analysis of visual data
