Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 97Image Retrieval and Classification TechniquesRadiomics and Machine Learning in Medical ImagingIndustrial Vision Systems and Defect Detection

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Evaluation of texture methods for image analysis

Sharma, M., Singh, S.·2001·The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001
DOI 10.1109/anziis.2001.974061
Open this paper in CoChat — free account

Abstract

The evaluation of texture features is important for several image processing applications. Texture analysis forms the basis of object recognition and classification in several domains. There is a range of texture extraction methods and their performance evaluation is an important part of understanding the utility of feature extraction tools in image analysis. In this paper we evaluate five different feature extraction methods. These are autocorrelation, edge frequency, primitive-length., Law's method, and co-occurrence matrices. All these methods are used for texture analysis of Meastex database. This is a publicly available database and therefore a meaningful comparison between the various methods is useful to our understanding of texture algorithms. Our results show that the Law's method and co-occurrence matrix method yield the best results. The overall best results;are obtained when we use features from all five methods. Results are produced using leave-one-out method.

Overview

Integrity signals

Registry

Crossref · 10.1109/anziis.2001.974061

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 1 references

Research works printed but not linked: 18

  1. 10.1016/0031-3203(95)00127-1 · DOI
  2. pratt, 1991, Digital Image Processing
  3. 10.1109/34.761261 · DOI
  4. 10.1006/cviu.1993.1024 · DOI
  5. 10.1016/0031-3203(94)90072-8 · DOI
  6. 10.1016/S0167-8655(97)00132-3 · DOI
  7. sonka, 1999, Image Processing Analysis and Machine Vision
  8. 10.1142/9789814343138_0010 · DOI
  9. gool van, 1985, Texture analysis, Computer Vision Graphics and Image Processing, 29, 336
  10. 10.1109/TSMC.1976.5408777 · DOI
  11. 10.1109/TSMC.1973.4309314 · DOI · Paper page
  12. 10.1049/ecej:19930013 · DOI
  13. laws, 1980, Textured image segmentation, Electrical Engineering
  14. 10.1016/0031-3203(96)00004-0 · DOI
  15. 10.1016/0031-3203(92)90036-I · DOI
  16. 0, Meastex Database
  17. 10.1109/TPAMI.1980.4767008 · DOI
  18. 10.1016/0031-3203(90)90017-F · DOI

Cited by

See all 97

Metadata

Authors
Sharma, M., & Singh, S.
Affiliations
University of Exeter (GB)
ORCID
Sharma, M.
Venue
The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001
Publisher
IEEE
Published
2001
Pages
117-121
DOI
10.1109/anziis.2001.974061
OpenAlex
W2091290950
Cited by
97
References
1
Citations by year
2024: 2 · 2023: 3 · 2022: 5 · 2021: 2 · 2020: 2 · 2019: 4 · 2018: 6 · 2017: 6 · 2016: 3 · 2015: 4 · 2014: 5 · 2013: 10 · 2012: 4
FWCI
3.79
Type
conference paper
Language
en
Open access
closed
Locations
Australian and New Zealand Intelligent Information Systems Conference (ANZIIS) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Keywords
texture analysis, texture feature extraction, autocorrelation, edge frequency, Laws texture features, co-occurrence matrix, leave-one-out cross-validation

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: Evaluation of texture methods for image analysis. https://app.cochat.ai/papers/10.1109/anziis.2001.974061
The paper
Sharma, M., & Singh, S. (2001). Evaluation of texture methods for image analysis. The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001. https://doi.org/10.1109/anziis.2001.974061

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.