ML Anthology

Morphological Color Quantization

Stuart E. Gibson, Richard W. Harvey
CVPR 2001 pp. II:525-530
doi:10.1109/CVPR.2001.991007 /cvpr/2001/gibson2001cvpr-morphological/

Abstract

Color histograms are a central feature in many image retrieval systems. Indeed they are part of the MPEG-7 standard. But histograms suffer from the "curse of dimensionality" in which the number of bins increases exponentially with the number of dimensions. There is therefore an imperative for methods for simplifying histograms. This paper presents a new method for simplifying histograms based on a cascade of increasing-scale graph morphology filters. The system we choose preserves scale space causality and so preserves the modes of the histogram. The method is quick to compute so is therefore a practically useful feature. We present results using the MPEG-7 Common Color Dataset that show that these new compressed features have a retrieval performance that is equivalent to full histograms.

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Cite

Text

Gibson and Harvey. "Morphological Color Quantization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.991007

Markdown

[Gibson and Harvey. "Morphological Color Quantization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/gibson2001cvpr-morphological/) doi:10.1109/CVPR.2001.991007

BibTeX

@inproceedings{gibson2001cvpr-morphological,
  title     = {{Morphological Color Quantization}},
  author    = {Gibson, Stuart E. and Harvey, Richard W.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2001},
  pages     = {II:525-530},
  doi       = {10.1109/CVPR.2001.991007},
  url       = {https://mlanthology.org/cvpr/2001/gibson2001cvpr-morphological/}
}