Abstract

The efficiency of texture image classification is certainly influenced by image scale when a feature space or a classification method is not scale invariant. An alternative approach to the scale-invariant techniques is proposed that first estimates an effective image scale and then uses it to adjust texture features to get the best possible texture image recognition and classification. We use the correlation distance between pixels as a measure of the scale of texture images. We study the performance of classification of texture images in the coordinated cluster representation (CCR) versus an image scale and the size of the scanning window used for the coordinated cluster transform. Given the number of classes to be classified in, we find that an optimal (up to 100%) classification efficiency in the CCR feature space is obtained by changing an image scale and∕or the size of the scanning window in the coordinated cluster transform.

© 2007 Optical Society of America

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  2. C. H. Chen, L. F. Pau, and P. S. P. Wang, eds., Handbook of Pattern Recognition and Computer Vision, 2nd ed. (World Scientific, 1996).
  3. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. (Wiley, 2001).
  4. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. (Academic, 1990).
  5. T. Y. Young and K.-S. Fu, eds., Handbook of Pattern Recognition and Image Processing (Academic, 1986).
  6. J. R. Berry and J. Goutsias, "A comparison of matrix texture features using a maximum likelihood texture classifier," in Visual Communication and Image Processing IV, Proc. SPIE 1199, 305-316 (1989).
  7. D. Chetverikov, "Texture analysis using feature-based pair wise interaction maps," Pattern Recogn. 32, 487-498 (1999).
    [CrossRef]
  8. I. M. Elfadel and R. W. Picard, "Gibbs random fields, co-occurrences and texture modeling," IEEE Trans. Pattern Anal. Mach. Intell. 16, 24-31 (1994).
    [CrossRef]
  9. A. Goon and J. P. Rolland, "Texture classification based on comparison of second-order statistics I: 2P-PDF estimation and distance measure," J. Opt. Soc. Am. A 16, 1566-1574 (1999).
    [CrossRef]
  10. R. M. Haralick, "Statistical and structural approaches to texture," Proc. IEEE 67, 786-804 (1979).
    [CrossRef]
  11. P. P. Ohanian and R. C. Dubes, "Performance evaluation for four classes of textural features," Pattern Recogn. 25, 819-833 (1992).
    [CrossRef]
  12. L. K. Soh and C. Tsatsoulis, "Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices," in IEEE Trans. Geosci. Remote Sens. 37, 780-787 (1999).
    [CrossRef]
  13. K. Valkealahti and E. Oja, "Reduced multidimensional co-occurrence histograms in texture analysis," in IEEE Trans. Pattern Anal. Mach. Intell. 20, 90-95 (1998).
    [CrossRef]
  14. R. Chellappa and S. Chatterjee, "Classification of textures using Gaussian Markov random fields," IEEE Trans. Acoust. Speech Signal Process. 33, 959-963 (1987).
    [CrossRef]
  15. M. R. Turner, "Texture discrimination by Gabor functions," Biol. Cybern. 55, 71-82 (1986).
    [PubMed]
  16. R. Azencott and J. P. Wang, "Texture classification using windowed Fourier filters," IEEE Trans. Pattern Anal. Mach. Intell. 19, 148-157 (1997).
    [CrossRef]
  17. D. Casasent and D. Psaltis, "Scale invariant optical transform," Opt. Eng. 15, 258-261 (1976).
  18. Y. Sheng and H. H. Arsenault, "Experiments on pattern recognition using invariant Fourier-Mellin descriptors," J. Opt. Soc. Am. A 3, 771-776 (1986).
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  20. O. Alata, C. Cariou, C. Ramananjarasoa, and M. Najim, "Classification of rotated and scaled textures using HMHV spectrum estimation and the Fourier-Mellin Transform," in 1998 International Conference on Image Processing-1 (IEEE, 1998), pp. 53-56.
  21. D. G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the International Conference on Computer Vision (ICCV), (Corfu, 1999), pp. 1150-1157.
  22. N. Gotze, S. Drue, and G. Hartmann, "Invariant object recognition with discriminant features based on local fast Fourier-Mellin transform," in Proceedings of the 15th International Conference on Pattern Recognition (IEEE, 2000), pp. 1948-1951.
  23. J. Alvarez-Borrego, R. R. Mouriño-Pérez, G. Cristóbal, and J. L. Pech-Pacheco, "Invariant recognition of polychromatic images of Vibrio cholerae O1," Opt. Eng. 41, 827-833 (2002).
    [CrossRef]
  24. G. Amu, S. Hasi, X. Yang, and Z. Ping, "Image analysis by pseudo-Jacobi (p = 4, q = 3)-Fourier moments," Appl. Opt. 43, 2093-2101 (2004).
    [CrossRef] [PubMed]
  25. M. M. Leung and A. M. Peterson, "Scale and rotation invariant texture classification," in Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems, and Computers, 1 (IEEE, 1992), pp. 461-465.
  26. E. V. Kurmyshev and R. E. Sánchez-Yáñez, "Texture classification based on image representation by coordinated clusters," in Proceedings of Image and Vision Computing (IEEE, 2001), pp. 213-217.
  27. R. E. Sánchez Yáñez, E. V. Kurmyshev, and F. J. Cuevas, "A framework for texture classification using the coordinated clusters representation," Pattern Recogn. Lett. 24, 21-31 (2003).
    [CrossRef]
  28. E. V. Kurmyshev, F. J. Cuevas, and R. E. Sánchez-Yáñez, "Noisy binary texture recognition using the coordinated cluster transform," Computación Sistemas 6, 196-203 (2003).
  29. R. E. Sánchez-Yáñez, E. V. Kurmyshev, and A. Fernández, "One-class texture classifier in the CCR feature space," Pattern Recogn. Lett. 24, 1503-1511 (2003).
    [CrossRef]
  30. E. V. Kurmyshev and R. E. Sánchez-Yáñez, "Comparative experiment with color texture classifiers using the CCR feature space," Pattern Recogn. Lett. 26, 1346-1353 (2005).
    [CrossRef]
  31. E. V. Kurmyshev and M. Cervantes, "A quasi-statistical approach to digital image representation," Rev. Mex. Fis. 42, 104-116 (1996).
  32. E. V. Kurmyshev and R. Soto, "Digital pattern recognition in the coordinated cluster representation," in Proceedings of the 1996 (IEEE) Nordic Signal Processing Simposium (IEEE, 1996), pp. 463-466.
  33. P. Brodatz, A Photographic Album for Artists and Designers (Dover, 1996).
  34. VisTex Vision Texture Database, http//-white.media.mit.edu/vismod/imagery/Vision Texture/Vistex.html (1997).
  35. These data are available at OuTex, Texture Database, http://www.outex.oulu.fi/temp/.
  36. T. Maenpaa and M. Pietikainen, "Classification with color and texture: jointly or separately," Pattern Recogn. 37, 1629-1640 (2004).
    [CrossRef]

2005

E. V. Kurmyshev and R. E. Sánchez-Yáñez, "Comparative experiment with color texture classifiers using the CCR feature space," Pattern Recogn. Lett. 26, 1346-1353 (2005).
[CrossRef]

2004

T. Maenpaa and M. Pietikainen, "Classification with color and texture: jointly or separately," Pattern Recogn. 37, 1629-1640 (2004).
[CrossRef]

G. Amu, S. Hasi, X. Yang, and Z. Ping, "Image analysis by pseudo-Jacobi (p = 4, q = 3)-Fourier moments," Appl. Opt. 43, 2093-2101 (2004).
[CrossRef] [PubMed]

2003

R. E. Sánchez Yáñez, E. V. Kurmyshev, and F. J. Cuevas, "A framework for texture classification using the coordinated clusters representation," Pattern Recogn. Lett. 24, 21-31 (2003).
[CrossRef]

E. V. Kurmyshev, F. J. Cuevas, and R. E. Sánchez-Yáñez, "Noisy binary texture recognition using the coordinated cluster transform," Computación Sistemas 6, 196-203 (2003).

R. E. Sánchez-Yáñez, E. V. Kurmyshev, and A. Fernández, "One-class texture classifier in the CCR feature space," Pattern Recogn. Lett. 24, 1503-1511 (2003).
[CrossRef]

2002

J. Alvarez-Borrego, R. R. Mouriño-Pérez, G. Cristóbal, and J. L. Pech-Pacheco, "Invariant recognition of polychromatic images of Vibrio cholerae O1," Opt. Eng. 41, 827-833 (2002).
[CrossRef]

1999

D. Chetverikov, "Texture analysis using feature-based pair wise interaction maps," Pattern Recogn. 32, 487-498 (1999).
[CrossRef]

A. Goon and J. P. Rolland, "Texture classification based on comparison of second-order statistics I: 2P-PDF estimation and distance measure," J. Opt. Soc. Am. A 16, 1566-1574 (1999).
[CrossRef]

L. K. Soh and C. Tsatsoulis, "Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices," in IEEE Trans. Geosci. Remote Sens. 37, 780-787 (1999).
[CrossRef]

1998

K. Valkealahti and E. Oja, "Reduced multidimensional co-occurrence histograms in texture analysis," in IEEE Trans. Pattern Anal. Mach. Intell. 20, 90-95 (1998).
[CrossRef]

1997

R. Azencott and J. P. Wang, "Texture classification using windowed Fourier filters," IEEE Trans. Pattern Anal. Mach. Intell. 19, 148-157 (1997).
[CrossRef]

1996

E. V. Kurmyshev and M. Cervantes, "A quasi-statistical approach to digital image representation," Rev. Mex. Fis. 42, 104-116 (1996).

P. S. Erbach and D. A. Gregory, "Scale invariant optical Mellin-wavelet joint transform correlation," in Optical Pattern Recognition VII, Proc. SPIE 2752, 69-77 (1996).

1994

I. M. Elfadel and R. W. Picard, "Gibbs random fields, co-occurrences and texture modeling," IEEE Trans. Pattern Anal. Mach. Intell. 16, 24-31 (1994).
[CrossRef]

1992

P. P. Ohanian and R. C. Dubes, "Performance evaluation for four classes of textural features," Pattern Recogn. 25, 819-833 (1992).
[CrossRef]

1989

J. R. Berry and J. Goutsias, "A comparison of matrix texture features using a maximum likelihood texture classifier," in Visual Communication and Image Processing IV, Proc. SPIE 1199, 305-316 (1989).

1987

R. Chellappa and S. Chatterjee, "Classification of textures using Gaussian Markov random fields," IEEE Trans. Acoust. Speech Signal Process. 33, 959-963 (1987).
[CrossRef]

1986

1979

R. M. Haralick, "Statistical and structural approaches to texture," Proc. IEEE 67, 786-804 (1979).
[CrossRef]

1976

D. Casasent and D. Psaltis, "Scale invariant optical transform," Opt. Eng. 15, 258-261 (1976).

Alata, O.

O. Alata, C. Cariou, C. Ramananjarasoa, and M. Najim, "Classification of rotated and scaled textures using HMHV spectrum estimation and the Fourier-Mellin Transform," in 1998 International Conference on Image Processing-1 (IEEE, 1998), pp. 53-56.

Alvarez-Borrego, J.

J. Alvarez-Borrego, R. R. Mouriño-Pérez, G. Cristóbal, and J. L. Pech-Pacheco, "Invariant recognition of polychromatic images of Vibrio cholerae O1," Opt. Eng. 41, 827-833 (2002).
[CrossRef]

Amu, G.

Arsenault, H. H.

Azencott, R.

R. Azencott and J. P. Wang, "Texture classification using windowed Fourier filters," IEEE Trans. Pattern Anal. Mach. Intell. 19, 148-157 (1997).
[CrossRef]

Berry, J. R.

J. R. Berry and J. Goutsias, "A comparison of matrix texture features using a maximum likelihood texture classifier," in Visual Communication and Image Processing IV, Proc. SPIE 1199, 305-316 (1989).

Brodatz, P.

P. Brodatz, A Photographic Album for Artists and Designers (Dover, 1996).

Cariou, C.

O. Alata, C. Cariou, C. Ramananjarasoa, and M. Najim, "Classification of rotated and scaled textures using HMHV spectrum estimation and the Fourier-Mellin Transform," in 1998 International Conference on Image Processing-1 (IEEE, 1998), pp. 53-56.

Casasent, D.

D. Casasent and D. Psaltis, "Scale invariant optical transform," Opt. Eng. 15, 258-261 (1976).

Cervantes, M.

E. V. Kurmyshev and M. Cervantes, "A quasi-statistical approach to digital image representation," Rev. Mex. Fis. 42, 104-116 (1996).

Chatterjee, S.

R. Chellappa and S. Chatterjee, "Classification of textures using Gaussian Markov random fields," IEEE Trans. Acoust. Speech Signal Process. 33, 959-963 (1987).
[CrossRef]

Chellappa, R.

R. Chellappa and S. Chatterjee, "Classification of textures using Gaussian Markov random fields," IEEE Trans. Acoust. Speech Signal Process. 33, 959-963 (1987).
[CrossRef]

Chen, C. H.

C. H. Chen, L. F. Pau, and P. S. P. Wang, eds., Handbook of Pattern Recognition and Computer Vision, 2nd ed. (World Scientific, 1996).

Chetverikov, D.

D. Chetverikov, "Texture analysis using feature-based pair wise interaction maps," Pattern Recogn. 32, 487-498 (1999).
[CrossRef]

Cristóbal, G.

J. Alvarez-Borrego, R. R. Mouriño-Pérez, G. Cristóbal, and J. L. Pech-Pacheco, "Invariant recognition of polychromatic images of Vibrio cholerae O1," Opt. Eng. 41, 827-833 (2002).
[CrossRef]

Cuevas, F. J.

R. E. Sánchez Yáñez, E. V. Kurmyshev, and F. J. Cuevas, "A framework for texture classification using the coordinated clusters representation," Pattern Recogn. Lett. 24, 21-31 (2003).
[CrossRef]

E. V. Kurmyshev, F. J. Cuevas, and R. E. Sánchez-Yáñez, "Noisy binary texture recognition using the coordinated cluster transform," Computación Sistemas 6, 196-203 (2003).

Drue, S.

N. Gotze, S. Drue, and G. Hartmann, "Invariant object recognition with discriminant features based on local fast Fourier-Mellin transform," in Proceedings of the 15th International Conference on Pattern Recognition (IEEE, 2000), pp. 1948-1951.

Dubes, R. C.

P. P. Ohanian and R. C. Dubes, "Performance evaluation for four classes of textural features," Pattern Recogn. 25, 819-833 (1992).
[CrossRef]

Duda, R. O.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. (Wiley, 2001).

Elfadel, I. M.

I. M. Elfadel and R. W. Picard, "Gibbs random fields, co-occurrences and texture modeling," IEEE Trans. Pattern Anal. Mach. Intell. 16, 24-31 (1994).
[CrossRef]

Erbach, P. S.

P. S. Erbach and D. A. Gregory, "Scale invariant optical Mellin-wavelet joint transform correlation," in Optical Pattern Recognition VII, Proc. SPIE 2752, 69-77 (1996).

Fernández, A.

R. E. Sánchez-Yáñez, E. V. Kurmyshev, and A. Fernández, "One-class texture classifier in the CCR feature space," Pattern Recogn. Lett. 24, 1503-1511 (2003).
[CrossRef]

Fu, K.-S.

T. Y. Young and K.-S. Fu, eds., Handbook of Pattern Recognition and Image Processing (Academic, 1986).

Fukunaga, K.

K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. (Academic, 1990).

Goon, A.

Gotze, N.

N. Gotze, S. Drue, and G. Hartmann, "Invariant object recognition with discriminant features based on local fast Fourier-Mellin transform," in Proceedings of the 15th International Conference on Pattern Recognition (IEEE, 2000), pp. 1948-1951.

Goutsias, J.

J. R. Berry and J. Goutsias, "A comparison of matrix texture features using a maximum likelihood texture classifier," in Visual Communication and Image Processing IV, Proc. SPIE 1199, 305-316 (1989).

Gregory, D. A.

P. S. Erbach and D. A. Gregory, "Scale invariant optical Mellin-wavelet joint transform correlation," in Optical Pattern Recognition VII, Proc. SPIE 2752, 69-77 (1996).

Haralick, R. M.

R. M. Haralick, "Statistical and structural approaches to texture," Proc. IEEE 67, 786-804 (1979).
[CrossRef]

Hart, P. E.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. (Wiley, 2001).

Hartmann, G.

N. Gotze, S. Drue, and G. Hartmann, "Invariant object recognition with discriminant features based on local fast Fourier-Mellin transform," in Proceedings of the 15th International Conference on Pattern Recognition (IEEE, 2000), pp. 1948-1951.

Hasi, S.

Jain, A. K.

M. Tuceryan and A. K. Jain, "Texture analysis," in Handbook of Pattern Recognition and Computer Vision, C.H. Chen, L.F. Pau, and P.S.P. Wang, eds. (World Scientific, 1993), pp. 235-276.

Kurmyshev, E. V.

E. V. Kurmyshev and R. E. Sánchez-Yáñez, "Comparative experiment with color texture classifiers using the CCR feature space," Pattern Recogn. Lett. 26, 1346-1353 (2005).
[CrossRef]

R. E. Sánchez Yáñez, E. V. Kurmyshev, and F. J. Cuevas, "A framework for texture classification using the coordinated clusters representation," Pattern Recogn. Lett. 24, 21-31 (2003).
[CrossRef]

R. E. Sánchez-Yáñez, E. V. Kurmyshev, and A. Fernández, "One-class texture classifier in the CCR feature space," Pattern Recogn. Lett. 24, 1503-1511 (2003).
[CrossRef]

E. V. Kurmyshev, F. J. Cuevas, and R. E. Sánchez-Yáñez, "Noisy binary texture recognition using the coordinated cluster transform," Computación Sistemas 6, 196-203 (2003).

E. V. Kurmyshev and M. Cervantes, "A quasi-statistical approach to digital image representation," Rev. Mex. Fis. 42, 104-116 (1996).

E. V. Kurmyshev and R. Soto, "Digital pattern recognition in the coordinated cluster representation," in Proceedings of the 1996 (IEEE) Nordic Signal Processing Simposium (IEEE, 1996), pp. 463-466.

E. V. Kurmyshev and R. E. Sánchez-Yáñez, "Texture classification based on image representation by coordinated clusters," in Proceedings of Image and Vision Computing (IEEE, 2001), pp. 213-217.

Leung, M. M.

M. M. Leung and A. M. Peterson, "Scale and rotation invariant texture classification," in Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems, and Computers, 1 (IEEE, 1992), pp. 461-465.

Lowe, D. G.

D. G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the International Conference on Computer Vision (ICCV), (Corfu, 1999), pp. 1150-1157.

Maenpaa, T.

T. Maenpaa and M. Pietikainen, "Classification with color and texture: jointly or separately," Pattern Recogn. 37, 1629-1640 (2004).
[CrossRef]

Mouriño-Pérez, R. R.

J. Alvarez-Borrego, R. R. Mouriño-Pérez, G. Cristóbal, and J. L. Pech-Pacheco, "Invariant recognition of polychromatic images of Vibrio cholerae O1," Opt. Eng. 41, 827-833 (2002).
[CrossRef]

Najim, M.

O. Alata, C. Cariou, C. Ramananjarasoa, and M. Najim, "Classification of rotated and scaled textures using HMHV spectrum estimation and the Fourier-Mellin Transform," in 1998 International Conference on Image Processing-1 (IEEE, 1998), pp. 53-56.

Ohanian, P. P.

P. P. Ohanian and R. C. Dubes, "Performance evaluation for four classes of textural features," Pattern Recogn. 25, 819-833 (1992).
[CrossRef]

Oja, E.

K. Valkealahti and E. Oja, "Reduced multidimensional co-occurrence histograms in texture analysis," in IEEE Trans. Pattern Anal. Mach. Intell. 20, 90-95 (1998).
[CrossRef]

Pau, L. F.

C. H. Chen, L. F. Pau, and P. S. P. Wang, eds., Handbook of Pattern Recognition and Computer Vision, 2nd ed. (World Scientific, 1996).

Pech-Pacheco, J. L.

J. Alvarez-Borrego, R. R. Mouriño-Pérez, G. Cristóbal, and J. L. Pech-Pacheco, "Invariant recognition of polychromatic images of Vibrio cholerae O1," Opt. Eng. 41, 827-833 (2002).
[CrossRef]

Peterson, A. M.

M. M. Leung and A. M. Peterson, "Scale and rotation invariant texture classification," in Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems, and Computers, 1 (IEEE, 1992), pp. 461-465.

Picard, R. W.

I. M. Elfadel and R. W. Picard, "Gibbs random fields, co-occurrences and texture modeling," IEEE Trans. Pattern Anal. Mach. Intell. 16, 24-31 (1994).
[CrossRef]

Pietikainen, M.

T. Maenpaa and M. Pietikainen, "Classification with color and texture: jointly or separately," Pattern Recogn. 37, 1629-1640 (2004).
[CrossRef]

Ping, Z.

Psaltis, D.

D. Casasent and D. Psaltis, "Scale invariant optical transform," Opt. Eng. 15, 258-261 (1976).

Ramananjarasoa, C.

O. Alata, C. Cariou, C. Ramananjarasoa, and M. Najim, "Classification of rotated and scaled textures using HMHV spectrum estimation and the Fourier-Mellin Transform," in 1998 International Conference on Image Processing-1 (IEEE, 1998), pp. 53-56.

Rolland, J. P.

Sánchez Yáñez, R. E.

R. E. Sánchez Yáñez, E. V. Kurmyshev, and F. J. Cuevas, "A framework for texture classification using the coordinated clusters representation," Pattern Recogn. Lett. 24, 21-31 (2003).
[CrossRef]

Sánchez-Yáñez, R. E.

E. V. Kurmyshev and R. E. Sánchez-Yáñez, "Comparative experiment with color texture classifiers using the CCR feature space," Pattern Recogn. Lett. 26, 1346-1353 (2005).
[CrossRef]

E. V. Kurmyshev, F. J. Cuevas, and R. E. Sánchez-Yáñez, "Noisy binary texture recognition using the coordinated cluster transform," Computación Sistemas 6, 196-203 (2003).

R. E. Sánchez-Yáñez, E. V. Kurmyshev, and A. Fernández, "One-class texture classifier in the CCR feature space," Pattern Recogn. Lett. 24, 1503-1511 (2003).
[CrossRef]

E. V. Kurmyshev and R. E. Sánchez-Yáñez, "Texture classification based on image representation by coordinated clusters," in Proceedings of Image and Vision Computing (IEEE, 2001), pp. 213-217.

Sheng, Y.

Soh, L. K.

L. K. Soh and C. Tsatsoulis, "Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices," in IEEE Trans. Geosci. Remote Sens. 37, 780-787 (1999).
[CrossRef]

Soto, R.

E. V. Kurmyshev and R. Soto, "Digital pattern recognition in the coordinated cluster representation," in Proceedings of the 1996 (IEEE) Nordic Signal Processing Simposium (IEEE, 1996), pp. 463-466.

Stork, D. G.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. (Wiley, 2001).

Tsatsoulis, C.

L. K. Soh and C. Tsatsoulis, "Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices," in IEEE Trans. Geosci. Remote Sens. 37, 780-787 (1999).
[CrossRef]

Tuceryan, M.

M. Tuceryan and A. K. Jain, "Texture analysis," in Handbook of Pattern Recognition and Computer Vision, C.H. Chen, L.F. Pau, and P.S.P. Wang, eds. (World Scientific, 1993), pp. 235-276.

Turner, M. R.

M. R. Turner, "Texture discrimination by Gabor functions," Biol. Cybern. 55, 71-82 (1986).
[PubMed]

Valkealahti, K.

K. Valkealahti and E. Oja, "Reduced multidimensional co-occurrence histograms in texture analysis," in IEEE Trans. Pattern Anal. Mach. Intell. 20, 90-95 (1998).
[CrossRef]

Wang, J. P.

R. Azencott and J. P. Wang, "Texture classification using windowed Fourier filters," IEEE Trans. Pattern Anal. Mach. Intell. 19, 148-157 (1997).
[CrossRef]

Wang, P. S. P.

C. H. Chen, L. F. Pau, and P. S. P. Wang, eds., Handbook of Pattern Recognition and Computer Vision, 2nd ed. (World Scientific, 1996).

Yang, X.

Young, T. Y.

T. Y. Young and K.-S. Fu, eds., Handbook of Pattern Recognition and Image Processing (Academic, 1986).

Appl. Opt.

Biol. Cybern.

M. R. Turner, "Texture discrimination by Gabor functions," Biol. Cybern. 55, 71-82 (1986).
[PubMed]

Computación Sistemas

E. V. Kurmyshev, F. J. Cuevas, and R. E. Sánchez-Yáñez, "Noisy binary texture recognition using the coordinated cluster transform," Computación Sistemas 6, 196-203 (2003).

IEEE Trans. Acoust. Speech Signal Process.

R. Chellappa and S. Chatterjee, "Classification of textures using Gaussian Markov random fields," IEEE Trans. Acoust. Speech Signal Process. 33, 959-963 (1987).
[CrossRef]

IEEE Trans. Geosci. Remote Sens.

L. K. Soh and C. Tsatsoulis, "Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices," in IEEE Trans. Geosci. Remote Sens. 37, 780-787 (1999).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

K. Valkealahti and E. Oja, "Reduced multidimensional co-occurrence histograms in texture analysis," in IEEE Trans. Pattern Anal. Mach. Intell. 20, 90-95 (1998).
[CrossRef]

R. Azencott and J. P. Wang, "Texture classification using windowed Fourier filters," IEEE Trans. Pattern Anal. Mach. Intell. 19, 148-157 (1997).
[CrossRef]

I. M. Elfadel and R. W. Picard, "Gibbs random fields, co-occurrences and texture modeling," IEEE Trans. Pattern Anal. Mach. Intell. 16, 24-31 (1994).
[CrossRef]

J. Opt. Soc. Am. A

Opt. Eng.

D. Casasent and D. Psaltis, "Scale invariant optical transform," Opt. Eng. 15, 258-261 (1976).

J. Alvarez-Borrego, R. R. Mouriño-Pérez, G. Cristóbal, and J. L. Pech-Pacheco, "Invariant recognition of polychromatic images of Vibrio cholerae O1," Opt. Eng. 41, 827-833 (2002).
[CrossRef]

Pattern Recogn.

T. Maenpaa and M. Pietikainen, "Classification with color and texture: jointly or separately," Pattern Recogn. 37, 1629-1640 (2004).
[CrossRef]

D. Chetverikov, "Texture analysis using feature-based pair wise interaction maps," Pattern Recogn. 32, 487-498 (1999).
[CrossRef]

P. P. Ohanian and R. C. Dubes, "Performance evaluation for four classes of textural features," Pattern Recogn. 25, 819-833 (1992).
[CrossRef]

Pattern Recogn. Lett.

R. E. Sánchez Yáñez, E. V. Kurmyshev, and F. J. Cuevas, "A framework for texture classification using the coordinated clusters representation," Pattern Recogn. Lett. 24, 21-31 (2003).
[CrossRef]

R. E. Sánchez-Yáñez, E. V. Kurmyshev, and A. Fernández, "One-class texture classifier in the CCR feature space," Pattern Recogn. Lett. 24, 1503-1511 (2003).
[CrossRef]

E. V. Kurmyshev and R. E. Sánchez-Yáñez, "Comparative experiment with color texture classifiers using the CCR feature space," Pattern Recogn. Lett. 26, 1346-1353 (2005).
[CrossRef]

Proc. IEEE

R. M. Haralick, "Statistical and structural approaches to texture," Proc. IEEE 67, 786-804 (1979).
[CrossRef]

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Figures (9)

Fig. 1
Fig. 1

Decimal code of BPU in the CCR calculation of a binary image.

Fig. 2
Fig. 2

Twelve source images of Rosa Porriño granite and anisotropic textures used in the generation of classes.

Fig. 3
Fig. 3

Maximal cross section of correlation function in the x direction of the image RP2_30R1 at R = 1, 0.8, and 0.6.

Fig. 4
Fig. 4

Maximal cross section of correlation function in the y direction of the image RP2_30R1 at R = 1, 0.8, and 0.6.

Fig. 5
Fig. 5

Average classification rate in eight classes versus a source image reduction, R = 1, 0.8, 0.6, 04, 0.2 at different values of γ = 0.25 , 0.313, 0.47, 0.50, 0.626, 0.75. The CCR scanning window is 3 × 3 pixels.

Fig. 6
Fig. 6

Average classification rate in eight classes versus a source image reduction, R = 1, 0.8, 0.6, 04, 0.2 at different values of γ = 0.187, 0.25, 0.50, 0.75. The CCR scanning window is 4 × 4 pixels.

Fig. 7
Fig. 7

Average classification rate in eight classes versus a source image reduction, R = 1, 0.8, 0.6, 04, 0.2 at different values of γ = 0.25 , 0.50, 0.75. The CCR scanning window is 5 × 5 pixels.

Fig. 8
Fig. 8

Classification rate in eight classes versus a source image reduction at γ = 0.50 for the CCR with the 3 × 3 , 4 × 4 , and 5 × 5 scanning window.

Fig. 9
Fig. 9

Average classification rate in 12 classes versus a source image reduction, R = 1, 0.8, 0.6, 04, 0.2 at different values of γ = 0.50 , 0.626, 0.75. The CCR scanning window is 3 × 3 pixels.

Tables (4)

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Table 1 Size of Subimages for Each Pair of Values (γ, R) Used in the Experiments with the 3 × 3 CCR Window

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Table 2 Size of Subimages for Each Pair of Values (γ, R) Used in the Experiments with the 4 × 4 CCR Window

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Table 3 Size of Subimages for Each Pair of Values (γ, R) Used in the Experiments with the 5 × 5 CCR Window

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Table 4 Correlation Distance Between Pixels of Source Images at Different Reduction R

Equations (8)

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A = [ 2 0 0 0 2 J 2 0 0 2 2 J 2 J 0 2 ( I 1 ) J 2 ( I 2 ) J 0 0 2 ( I 1 ) J 0 0 0 2 ( I 1 ) J ] ,
B = [ 2 0 2 1 2 J 1 0 0 0 0 2 0 2 J 2 2 J 1 0 0 0 0 0 0 2 J 2 2 J 1 ] .
S ˜ = A · S · B ,
s ( l , m ) s ( l + l 1 , m + m 1 ) s ( l + l k 1 , m + m k 1 ) = lim L , M W 1 l , m = 1 L , M s ( l , m ) s ( l + l 1 , m + m 1 ) s ( l + l k 1 , m + m k 1 )
3 I 2 max d x , 3 J 2 max d y ,
C ( l , m ) = K 1 i = 1 L j = 1 M S ( i , j ) S ( i + l , j + m ) ,
C ˜ ( l , m ) = K ˜ 1 i = 1 L / α j = 1 M / β S ˜ ( i , j ) S ˜ ( i + l , j + m ) , = K ˜ 1 i = 1 L / α j = 1 M / β S ( α i , β j ) S ( α ( i + l ) , β ( j + m ) ) , K 1 i = α L j = β M S ( i , j ) S ( i + α l , j + β m ) C ( α l , β m ) ,
K ˜ = i = 1 L / α j = 1 M / β S ˜ 2 ( i , j ) = i = 1 L / α j = 1 M / β S 2 ( α i , β j ) = i = α L j = β M S 2 ( i , j ) K .

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