The DOI matches its Crossref record.
A graph distance measure for image analysis
Abstract
Attributed relational graphs (ARGs) have shown superior qualities when used for image representation and analysis in computer vision systems. A new, efficient approach for calculating a global distance measure between attributed relational graphs is proposed, and its applications in computer vision are discussed. The distance measure is calculated by a global optimization algorithm that is shown to be very efficient for this problem. The approach shows good results for practical size ARGs. The technique is also suitable for parallel processing implementation.
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
A graduated assignment algorithm for graph matching
Gold, S., & Rangarajan, A. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.491619 · Cited by 1,090
Metadata
- Authors
- Eshera, M. A., & Fu, K.-S.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics
- Published
- 1984-05
- Volume
- SMC-14
- Issue
- 3
- Pages
- 398-408
- ISSN
- 0018-9472 (ISSN-L)
- 2168-2909
- OpenAlex
- W2013563330
- Cited by
- 185
- References
- 0
- FWCI
- 1.18
- Type
- journal article
- 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 graph distance measure for image analysis. https://app.cochat.ai/papers/10.1109/tsmc.1984.6313232
- The paper
- Eshera, M. A., & Fu, K.-S. (1984). A graph distance measure for image analysis. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/tsmc.1984.6313232
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.
