Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension

Saad Al-Momen; Loay E. George; Raid K. Naji

doi:doi:10.11648/j.acm.20150401.12

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Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension

Saad Al-Momen, Loay E. George, Raid K. Naji

Published in Applied and Computational Mathematics (Volume 4, Issue 1)

Received: 26 December 2014 Accepted: 15 January 2015 Published: 27 January 2015

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Abstract

Feature extraction is an important process for texture classification. This paper suggests two sets of features for texture analysis. In the first set of features, a set of fractal features is obtained from the eight wavelet sub-bands that are generated by applying Haar wavelet transform twice times according to dyadic architecture. The fractal features are determined using the differential box counting method. While for determining the second set of features, the cubic spline representation is applied to decompose the image signal into rough and smooth components; then applying the wavelet transform and finally compute the fractal dimension for all the sub-bands of both images. Each type of these two extracted feature sets is studied individually, and they are used together. Their overall performance is investigated. The proposed features set has been applied on two texture datasets, one consists of textures with directional properties, and the second set consists of textures samples that have directional attributes. The test results showed that the proposed methods give a high level of classification with images that have or do not have directional properties.

Published in	Applied and Computational Mathematics (Volume 4, Issue 1)
DOI	10.11648/j.acm.20150401.12
Page(s)	5-10
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Texture Classification, Texture Analysis, Fractal, Wavelet Features, Cubic Spline

References

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APA Style

Saad Al-Momen, Loay E. George, Raid K. Naji. (2015). Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension. Applied and Computational Mathematics, 4(1), 5-10. https://doi.org/10.11648/j.acm.20150401.12

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ACS Style

Saad Al-Momen; Loay E. George; Raid K. Naji. Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension. Appl. Comput. Math. 2015, 4(1), 5-10. doi: 10.11648/j.acm.20150401.12

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AMA Style

Saad Al-Momen, Loay E. George, Raid K. Naji. Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension. Appl Comput Math. 2015;4(1):5-10. doi: 10.11648/j.acm.20150401.12

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@article{10.11648/j.acm.20150401.12,
  author = {Saad Al-Momen and Loay E. George and Raid K. Naji},
  title = {Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension},
  journal = {Applied and Computational Mathematics},
  volume = {4},
  number = {1},
  pages = {5-10},
  doi = {10.11648/j.acm.20150401.12},
  url = {https://doi.org/10.11648/j.acm.20150401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20150401.12},
  abstract = {Feature extraction is an important process for texture classification. This paper suggests two sets of features for texture analysis. In the first set of features, a set of fractal features is obtained from the eight wavelet sub-bands that are generated by applying Haar wavelet transform twice times according to dyadic architecture. The fractal features are determined using the differential box counting method. While for determining the second set of features, the cubic spline representation is applied to decompose the image signal into rough and smooth components; then applying the wavelet transform and finally compute the fractal dimension for all the sub-bands of both images. Each type of these two extracted feature sets is studied individually, and they are used together. Their overall performance is investigated. The proposed features set has been applied on two texture datasets, one consists of textures with directional properties, and the second set consists of textures samples that have directional attributes. The test results showed that the proposed methods give a high level of classification with images that have or do not have directional properties.},
 year = {2015}
}

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TY - JOUR
T1 - Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension
AU - Saad Al-Momen
AU - Loay E. George
AU - Raid K. Naji
Y1 - 2015/01/27
PY - 2015
N1 - https://doi.org/10.11648/j.acm.20150401.12
DO - 10.11648/j.acm.20150401.12
T2 - Applied and Computational Mathematics
JF - Applied and Computational Mathematics
JO - Applied and Computational Mathematics
SP - 5
EP - 10
PB - Science Publishing Group
SN - 2328-5613
UR - https://doi.org/10.11648/j.acm.20150401.12
AB - Feature extraction is an important process for texture classification. This paper suggests two sets of features for texture analysis. In the first set of features, a set of fractal features is obtained from the eight wavelet sub-bands that are generated by applying Haar wavelet transform twice times according to dyadic architecture. The fractal features are determined using the differential box counting method. While for determining the second set of features, the cubic spline representation is applied to decompose the image signal into rough and smooth components; then applying the wavelet transform and finally compute the fractal dimension for all the sub-bands of both images. Each type of these two extracted feature sets is studied individually, and they are used together. Their overall performance is investigated. The proposed features set has been applied on two texture datasets, one consists of textures with directional properties, and the second set consists of textures samples that have directional attributes. The test results showed that the proposed methods give a high level of classification with images that have or do not have directional properties.
VL - 4
IS - 1
ER -

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Author Information

Saad Al-Momen

Mathematics Department, College of Science, Baghdad University, Baghdad, IRAQ
Loay E. George

Computer Science Department, College of Science, Baghdad University, Baghdad, IRAQ
Raid K. Naji

Mathematics Department, College of Science, Baghdad University, Baghdad, IRAQ

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Plain Text BibTeX RIS

APA Style

Saad Al-Momen, Loay E. George, Raid K. Naji. (2015). Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension. Applied and Computational Mathematics, 4(1), 5-10. https://doi.org/10.11648/j.acm.20150401.12

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ACS Style

Saad Al-Momen; Loay E. George; Raid K. Naji. Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension. Appl. Comput. Math. 2015, 4(1), 5-10. doi: 10.11648/j.acm.20150401.12

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AMA Style

Saad Al-Momen, Loay E. George, Raid K. Naji. Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension. Appl Comput Math. 2015;4(1):5-10. doi: 10.11648/j.acm.20150401.12

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@article{10.11648/j.acm.20150401.12,
  author = {Saad Al-Momen and Loay E. George and Raid K. Naji},
  title = {Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension},
  journal = {Applied and Computational Mathematics},
  volume = {4},
  number = {1},
  pages = {5-10},
  doi = {10.11648/j.acm.20150401.12},
  url = {https://doi.org/10.11648/j.acm.20150401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20150401.12},
  abstract = {Feature extraction is an important process for texture classification. This paper suggests two sets of features for texture analysis. In the first set of features, a set of fractal features is obtained from the eight wavelet sub-bands that are generated by applying Haar wavelet transform twice times according to dyadic architecture. The fractal features are determined using the differential box counting method. While for determining the second set of features, the cubic spline representation is applied to decompose the image signal into rough and smooth components; then applying the wavelet transform and finally compute the fractal dimension for all the sub-bands of both images. Each type of these two extracted feature sets is studied individually, and they are used together. Their overall performance is investigated. The proposed features set has been applied on two texture datasets, one consists of textures with directional properties, and the second set consists of textures samples that have directional attributes. The test results showed that the proposed methods give a high level of classification with images that have or do not have directional properties.},
 year = {2015}
}

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