Empirical evaluation of dissimilarity measures for color and texture


Journal Article

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.

Duke Authors

Cited Authors

  • Puzicha, J; Buhmann, JM; Rubner, Y; Tomasi, C

Published Date

  • December 1, 1999

Published In

  • Proceedings of the Ieee International Conference on Computer Vision

Volume / Issue

  • 2 /

Start / End Page

  • 1165 - 1172

Citation Source

  • Scopus