The Earth Mover's Distance Under Transformation Sets
Abstract
The Earth Mover's Distance (EMD) is a distance measure between distributions with applications in image retrieval and matching. We consider the problem of computing a transformation of one distribution which minimizes its EMD to another. The applications discussed here include estimation of the size at which a color pattern occurs in an image, lighting-invariant object recognition, and point feature matching in stereo image pairs. We present a monotonically convergent iteration which can be applied to a large class of EMD under transformation problems, although the iteration may converge to only a locally optimal transformation. We also provide algorithms that are guaranteed to compute a globally optimal transformation for a few specific problems, including some EMD under translation problems.
Cite
Text
Cohen and Guibas. "The Earth Mover's Distance Under Transformation Sets." IEEE/CVF International Conference on Computer Vision, 1999. doi:10.1109/ICCV.1999.790393Markdown
[Cohen and Guibas. "The Earth Mover's Distance Under Transformation Sets." IEEE/CVF International Conference on Computer Vision, 1999.](https://mlanthology.org/iccv/1999/cohen1999iccv-earth/) doi:10.1109/ICCV.1999.790393BibTeX
@inproceedings{cohen1999iccv-earth,
title = {{The Earth Mover's Distance Under Transformation Sets}},
author = {Cohen, Scott D. and Guibas, Leonidas J.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1999},
pages = {1076-1083},
doi = {10.1109/ICCV.1999.790393},
url = {https://mlanthology.org/iccv/1999/cohen1999iccv-earth/}
}