Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 255

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Efficient use of local edge histogram descriptor

Park, Dong Kwon, Jeon, Yoon Seok, Won, Chee Sun·2000·Proceedings of the 2000 ACM workshops on Multimedia

Abstract

The purpose of this paper is to show how the edge histogram descriptor for MPEG-7 can be efficiently utilized for image matching. Since the edge histogram descriptor recommended for the MPEG-7 standard represents only local edge distribution in an image, the matching performance for image retrieval may not be satisfactory. In this paper, to increase the matching performance, we propose to use the global and semi-local edge histograms generated directly from the local histogram bins. Then, the global, semi-global, and local histograms of two images are compared to evaluate the similarity measure. Since we exploit the absolute locations of edge in the image as well as its global composition, the proposed matching method is considered to be a more image content-based retrieval. Experimental results support this claim. Experiments on test images for MPEG-7 core experiment show that the proposed method yields better retrieval performance especially for semantic similarity.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/357744.357758

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Cites

Show all 2 references

Cited by

See all 255

Metadata

Authors
Park, D. K., Jeon, Y. S., & Won, C. S.
Venue
Proceedings of the 2000 ACM workshops on Multimedia
Publisher
ACM
Published
2000-11-04
Pages
51-54
OpenAlex
W2042241913
Cited by
255
References
2
FWCI
2.11
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: Efficient use of local edge histogram descriptor. https://app.cochat.ai/papers/10.1145/357744.357758
The paper
Park, D. K., Jeon, Y. S., & Won, C. S. (2000). Efficient use of local edge histogram descriptor. Proceedings of the 2000 ACM workshops on Multimedia. https://doi.org/10.1145/357744.357758

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.