Title :
Robust analysis of feature spaces: color image segmentation
Author :
Comaniciu, Dorin ; Meer, Peter
Author_Institution :
Dept. of Electr. & Comput. Eng., Rutgers Univ., Piscataway, NJ, USA
Abstract :
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 512×512 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
Keywords :
computer vision; image segmentation; color image segmentation; color images; content-based query systems; density gradients estimation; feature spaces; gray level images; high quality edge image; image features; lightness coordinate; mean shift algorithm; nonparametric procedure; preprocessor; robust analysis; robust clustering; Clustering algorithms; Computer vision; Ellipsoids; Image analysis; Image color analysis; Image segmentation; Probability distribution; Robustness; Shape; Statistical distributions;
Conference_Titel :
Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on
Conference_Location :
San Juan
Print_ISBN :
0-8186-7822-4
DOI :
10.1109/CVPR.1997.609410