Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 364

The DOI matches its Crossref record.

Efficient and Robust Retrieval by Shape Content through Curvature Scale Space

Mokhtarian, Farzin, Abbasi, Sadegh, Kittler, Josef·1998·Series on Software Engineering and Knowledge Engineering

Abstract

. We introduce a very fast and reliable method for shape similarity retrieval in large image databases which is robust with respect to noise, scale and orientation changes of the objects. The maxima of curvature zero crossing contours of Curvature Scale Space (CSS) image are used to represent the shapes of object boundary contours. While a complex boundary is represented by about five pairs of integer values, an effective indexing method based on the aspect ratio of the CSS image, eccentricity and circularity is used to narrow down the range of searching. Since the matching algorithm has been designed to use global information, it is sensitive to major occlusion, but some minor occlusion will not cause any problems. We have tested and evaluated our method on a prototype database of 450 images of marine animals with a vast variety of shapes with very good results. The method can either be used in real applications or produce a reliable shape description for more complicated images when ...

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 · 10.1142/9789812797988_0005

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.

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

Cited by

See all 364

Metadata

Authors
Mokhtarian, F., Abbasi, S., & Kittler, J.
Venue
Series on Software Engineering and Knowledge Engineering
Published
1998-01
Pages
51-58
ISSN
1793-0995 (ISSN-L)
OpenAlex
W1601443878
Cited by
364
References
0
FWCI
26.83
Type
book chapter
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 and Robust Retrieval by Shape Content through Curvature Scale Space. https://app.cochat.ai/papers/10.1142/9789812797988_0005
The paper
Mokhtarian, F., Abbasi, S., & Kittler, J. (1998). Efficient and Robust Retrieval by Shape Content through Curvature Scale Space. Series on Software Engineering and Knowledge Engineering. https://doi.org/10.1142/9789812797988_0005

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.