Empirical Evaluation of Dissimilarity Measures for Color and Texture

Jan Puzicha, Yossi Rubner, Carlo Tomasi, Joachim M. Buhmann

ICCV 1999 pp. 1165-1172

doi:10.1109/ICCV.1999.790412 /iccv/1999/puzicha1999iccv-empirical/

Abstract

This paper empirically compares nine image dissimilarity measures that are based on distributions of color and texture features summarizing over 1,000 CPU hours of computational experiments. Ground truth is collected via a novel random sampling scheme for color and via an image partitioning method for texture. Quantitative performance evaluations are given for classification, image retrieval, and segmentation tasks, and for a wide variety of dissimilarity measures. It is demonstrated how the selection of a measure, based on large scale evaluation, substantially improves the quality of classification, retrieval, and unsupervised segmentation of color and texture images.

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Cite

Text

Puzicha et al. "Empirical Evaluation of Dissimilarity Measures for Color and Texture." IEEE/CVF International Conference on Computer Vision, 1999. doi:10.1109/ICCV.1999.790412

Markdown

[Puzicha et al. "Empirical Evaluation of Dissimilarity Measures for Color and Texture." IEEE/CVF International Conference on Computer Vision, 1999.](https://mlanthology.org/iccv/1999/puzicha1999iccv-empirical/) doi:10.1109/ICCV.1999.790412

BibTeX

@inproceedings{puzicha1999iccv-empirical,
  title     = {{Empirical Evaluation of Dissimilarity Measures for Color and Texture}},
  author    = {Puzicha, Jan and Rubner, Yossi and Tomasi, Carlo and Buhmann, Joachim M.},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {1999},
  pages     = {1165-1172},
  doi       = {10.1109/ICCV.1999.790412},
  url       = {https://mlanthology.org/iccv/1999/puzicha1999iccv-empirical/}
}