Color image segmentation based on automatic morphological clustering

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Abstract

We present an original method to segment color images using a classification in the 3-D color space. In the case of ordinary images, clusters that appear in 3-D histograms usually do not fit a well-known statistical model. For that reason, we propose a classifier that relies on mathematical morphology, and more precisely on the watershed algorithm. We show on various images that the expected color clusters are correctly identified by our method. Last, to segment color images into coherent regions, we perform a Markovian labeling that takes advantage of the morphological classification results.

Documents

Bibtex (lrde.bib)

@InProceedings{	  geraud.01.icip,
  author	= {Thierry G\'eraud and Pierre-Yves Strub and J\'er\^ome
		  Darbon},
  title		= {Color image segmentation based on automatic morphological
		  clustering},
  booktitle	= {Proceedings of the IEEE International Conference on Image
		  Processing (ICIP)},
  year		= 2001,
  volume	= 3,
  pages		= {70--73},
  address	= {Thessaloniki, Greece},
  month		= oct,
  abstract	= {We present an original method to segment color images
		  using a classification in the 3-D color space. In the case
		  of ordinary images, clusters that appear in 3-D histograms
		  usually do not fit a well-known statistical model. For that
		  reason, we propose a classifier that relies on mathematical
		  morphology, and more precisely on the watershed algorithm.
		  We show on various images that the expected color clusters
		  are correctly identified by our method. Last, to segment
		  color images into coherent regions, we perform a Markovian
		  labeling that takes advantage of the morphological
		  classification results.}
}