Research Article
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Year 2016, Special Issue (2016), 338 - 341, 01.12.2016
https://doi.org/10.18100/ijamec.270683

Abstract

References

  • J. Raju ve C. Durai, A Survey on Texture Classification Techniques, Information Communication and Embedded Systems (ICICES), Chennai, 2013.
  • T. Ojala, M. Pietikainen ve D. Harwood, A Comparative Study of Texture Measures with Classification Based on Feature Distributions, Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996.
  • X. Huang, S. Li ve Y. Wang, Shape localization based on statistical method using extended local binary pattern, Proc.Int.Conf.Image Graph., 2004.
  • M. Heikkila, M. Pietikainen ve C. Schmid, Decription of interest regions with local binary patterns, Pattern Recognition, vol. 42, pp. 425-436, 2009.
  • S. C., Learning local binary patterns for gender classification on real-world face images, Pattern Recognition Letters, vol. 33, pp. 431-437, 2012.
  • M. G. K. Ong, T. Connie ve T. A. B. Jin, Touch-less palm print biometrics: Novel design and implementation, Image and Vision Computing, vol. 26, pp. 1551-1560, 2008.
  • B. Kir, M. Kurt ve O. Urhan, Local Binary Pattern Based Fast Digital Image Stabilization, IEEE Signal Processing Letters, vol. 22, pp. 341-345, 2015.
  • S.-M. Huang ve J.-F. Yang, Linear Discriminant Regression Classification for Face Recognition, IEEE Signal Processing Letters, vol. 20, no. 1, pp. 91-94, 2013.

Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition

Year 2016, Special Issue (2016), 338 - 341, 01.12.2016
https://doi.org/10.18100/ijamec.270683

Abstract

In many texture recognition problems, Local Binary
Patterns (LBP) method is used for feature extraction. This method is based on
comparison of each centre pixel and its neighbours’ intensity value in image.
Due to its simplicity of calculation, LBP has become one of the most popular
feature extraction techniques. In literature, different neighbourhood
topologies of LBP structure are given such as circle, square, ellipse,
parabola, hyperbola, and Archimedean spiral. This paper focuses on the use of
uniform and basic LBP that have spiral topology in texture classification. We
first derive basic and uniform LBP features based on spiral topology. Then the
performances of several classification methods such as linear discriminant analysis
(LDA), linear regression classifier (LRC), support vector machines (SVM),
Chi-square test, and G-test are compared using these features in UIUC texture
database.

References

  • J. Raju ve C. Durai, A Survey on Texture Classification Techniques, Information Communication and Embedded Systems (ICICES), Chennai, 2013.
  • T. Ojala, M. Pietikainen ve D. Harwood, A Comparative Study of Texture Measures with Classification Based on Feature Distributions, Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996.
  • X. Huang, S. Li ve Y. Wang, Shape localization based on statistical method using extended local binary pattern, Proc.Int.Conf.Image Graph., 2004.
  • M. Heikkila, M. Pietikainen ve C. Schmid, Decription of interest regions with local binary patterns, Pattern Recognition, vol. 42, pp. 425-436, 2009.
  • S. C., Learning local binary patterns for gender classification on real-world face images, Pattern Recognition Letters, vol. 33, pp. 431-437, 2012.
  • M. G. K. Ong, T. Connie ve T. A. B. Jin, Touch-less palm print biometrics: Novel design and implementation, Image and Vision Computing, vol. 26, pp. 1551-1560, 2008.
  • B. Kir, M. Kurt ve O. Urhan, Local Binary Pattern Based Fast Digital Image Stabilization, IEEE Signal Processing Letters, vol. 22, pp. 341-345, 2015.
  • S.-M. Huang ve J.-F. Yang, Linear Discriminant Regression Classification for Face Recognition, IEEE Signal Processing Letters, vol. 20, no. 1, pp. 91-94, 2013.
There are 8 citations in total.

Details

Subjects Engineering
Journal Section Research Article
Authors

Nihan Kazak

Publication Date December 1, 2016
Published in Issue Year 2016 Special Issue (2016)

Cite

APA Kazak, N. (2016). Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition. International Journal of Applied Mathematics Electronics and Computers(Special Issue-1), 338-341. https://doi.org/10.18100/ijamec.270683
AMA Kazak N. Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition. International Journal of Applied Mathematics Electronics and Computers. December 2016;(Special Issue-1):338-341. doi:10.18100/ijamec.270683
Chicago Kazak, Nihan. “Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition”. International Journal of Applied Mathematics Electronics and Computers, no. Special Issue-1 (December 2016): 338-41. https://doi.org/10.18100/ijamec.270683.
EndNote Kazak N (December 1, 2016) Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 338–341.
IEEE N. Kazak, “Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition”, International Journal of Applied Mathematics Electronics and Computers, no. Special Issue-1, pp. 338–341, December 2016, doi: 10.18100/ijamec.270683.
ISNAD Kazak, Nihan. “Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition”. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 (December 2016), 338-341. https://doi.org/10.18100/ijamec.270683.
JAMA Kazak N. Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition. International Journal of Applied Mathematics Electronics and Computers. 2016;:338–341.
MLA Kazak, Nihan. “Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition”. International Journal of Applied Mathematics Electronics and Computers, no. Special Issue-1, 2016, pp. 338-41, doi:10.18100/ijamec.270683.
Vancouver Kazak N. Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition. International Journal of Applied Mathematics Electronics and Computers. 2016(Special Issue-1):338-41.