Robust analysis of feature spaces: color image segmentation

• Published in: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 750 - 755
• Main Authors: Comaniciu, D., Meer, P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1997
• Subjects: Clustering algorithms; Computer vision; Ellipsoids; Image analysis; Image color analysis; Image segmentation; Probability distribution; Robustness; Shape; Statistical distributions
• ISBN: 9780818678226; 0818678224
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.1997.609410

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 512/spl times/512 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.