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Robust Analysis of Feature Spaces: Color Image Segmentation (1997)
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BibTeX
@INPROCEEDINGS{Comaniciu97robustanalysis,
author = {Dorin Comaniciu and Peter Meer},
title = {Robust Analysis of Feature Spaces: Color Image Segmentation},
booktitle = {},
year = {1997},
pages = {750--755},
publisher = {}
}
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Abstract
A general technique for the recovery of significant image features is presented. The technique is basedon the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Featurespace of any naturecan beprocessed, 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 x 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.
Keyphrases
color image segmentation robust analysis feature space color image standard workstation simple nonparametric procedure current method robust clustering general technique gray level image content-based query system density gradient mean shift algorithm lightness coordinate high quality edge image significant image feature significant color
