Model-based halftoning for color image segmentation. Puzicha, J. and Belongie, S. In Pattern Recognition, 2000. Proceedings. 15th International Conference on, volume 3, pages 629 -632 vol.3, 2000.
doi  abstract   bibtex   
Grouping algorithms based on histograms over measured image features have very successfully been applied to textured image segmentation. However, the competing goals of statistical estimation significance demanding few quantization levels versus the necessary richness in representation often prevent a successful application for the color cue, since quantization may result in contouring. We combine a halftoning technique called spatial quantization with distribution-based grouping algorithms to synthesize a powerful color image segmentation technique. The spatial quantization simultaneously determines color palette and halftoning by optimization of a joint cost function. It therefore allows for a highly adapted image representation with a smooth transition of color distributions for non-constant image surfaces
@inproceedings{903624,
	Author = {Puzicha, J. and Belongie, S.},
	Booktitle = {Pattern Recognition, 2000. Proceedings. 15th International Conference on},
	Date-Added = {2012-08-20 14:09:01 +0000},
	Date-Modified = {2012-08-20 14:09:01 +0000},
	Doi = {10.1109/ICPR.2000.903624},
	Issn = {1051-4651},
	Keywords = {color cue;color distributions;color image segmentation;color palette;distribution-based grouping algorithms;highly adapted image representation;model-based halftoning;nonconstant image surfaces;spatial quantization;computer vision;image colour analysis;image representation;image segmentation;quantisation (signal);},
	Pages = {629 -632 vol.3},
	Title = {Model-based halftoning for color image segmentation},
	Volume = {3},
	Year = {2000},
	Abstract = {Grouping algorithms based on histograms over measured image features have very successfully been applied to textured image segmentation. However, the competing goals of statistical estimation significance demanding few quantization levels versus the necessary richness in representation often prevent a successful application for the color cue, since quantization may result in contouring. We combine a halftoning technique called spatial quantization with distribution-based grouping algorithms to synthesize a powerful color image segmentation technique. The spatial quantization simultaneously determines color palette and halftoning by optimization of a joint cost function. It therefore allows for a highly adapted image representation with a smooth transition of color distributions for non-constant image surfaces},
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