The DOI matches its Crossref record.
A metric for distributions with applications to image databases
Abstract
We introduce a new distance between two distributions that we call the Earth Mover's Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distribution into the other by moving "distribution mass" around. This is a special case of the transportation problem from linear optimization, for which efficient algorithms are available. The EMD also allows for partial matching. When used to compare distributions that have the same overall mass, the EMD is a true metric, and has easy-to-compute lower bounds. In this paper we focus on applications to image databases, especially color and texture. We use the EMD to exhibit the structure of color-distribution and texture spaces by means of Multi-Dimensional Scaling displays. We also propose a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00025 · Cited by 3,422
BERTScore: Evaluating Text Generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865
Simultaneous localization and mapping (SLAM): part II
Bailey, T., & Durrant-Whyte, H. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1678144 · Cited by 2,554
A survey of content-based image retrieval with high-level semantics
Liu, Y., Zhang, D., Lu, G., & Ma, W.-Y. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.04.045 · Cited by 1,696
Visualizing structure and transitions in high-dimensional biological data
Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., Elzen, A. V. D., Hirn, M. J., Coifman, R. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0336-3 · Cited by 1,065
Metadata
- Authors
- Rubner, Y., Tomasi, C., & Guibas, L. J.
- Venue
- Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271)
- Publisher
- Narosa Publishing House
- Published
- 2002-11-27
- Pages
- 59-66
- OpenAlex
- W2125101937
- Cited by
- 1,726
- References
- 0
- FWCI
- 32.58
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A metric for distributions with applications to image databases. https://app.cochat.ai/papers/10.1109/iccv.1998.710701
- The paper
- Rubner, Y., Tomasi, C., & Guibas, L. J. (2002). A metric for distributions with applications to image databases. Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271). https://doi.org/10.1109/iccv.1998.710701
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
