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.
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