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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
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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.

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Crossref · 10.1109/anziis.2001.974061

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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

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

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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.