Empirical evaluation of dissimilarity measures for color and texture

@inproceedings{citeulike:1775252, 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}, author = {Puzicha, J. and Buhmann, J. M. and Rubner, Y. and Tomasi, C.}, booktitle = {Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on}, citeulike-article-id = {1775252}, doi = {10.1109/ICCV.1999.790412}, journal = {Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on}, keywords = {divergences, empirical-evaluation}, pages = {1165--1172 vol.2}, posted-at = {2007-11-06 02:13:20}, priority = {2}, title = {Empirical evaluation of dissimilarity measures for color and texture}, url = {http://dx.doi.org/10.1109/ICCV.1999.790412}, volume = {2}, year = {1999} }

TY - CONF ID - citeulike:1775252 L3 - citeulike-article-id:1775252 N2 - 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 JF - Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on EP - 1172 vol.2 TI - Empirical evaluation of dissimilarity measures for color and texture VL - 2 T2 - Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on SP - 1165 KW - divergences KW - empirical-evaluation AU - Puzicha, J AU - Buhmann, JM AU - Rubner, Y AU - Tomasi, C PY - 1999/// UR - http://dx.doi.org/10.1109/ICCV.1999.790412 ER -