access icon free Texture classification by multifractal spectrum and barycentric coordinates of bit planes of wavelet coefficients

© Crown copyright
Received 21/10/2016, Accepted 30/06/2017, Revised 12/06/2017, Published 24/07/2017

A new texture classification method based on wavelet transform is presented. The elements of the signature vector, FDBC, of an image are the fractal dimensions and barycentric coordinates of the bit planes of the wavelet coefficients in both the three-level high-frequency domains and the third low-frequency domain. The pretreatment is done with SVD decomposition and reconstruction by dropping half singular values. The one-nearest-neighbour classifier (1NN) with distance is used to make the classification. Furthermore, to improve classification result, the classifier 1NN is strengthened with weighted distance. The proposed method is tested on five subsets from Brodatz database and UMD database and is experimentally proved more efficient and more promising.

Inspec keywords: fractals; image classification; spectral analysis; singular value decomposition; wavelet transforms; image reconstruction; image texture

Other keywords: multifractal spectrum; signature vector; bit planes; texture classification; 1NN classifier; fractal dimensions; FDBC; wavelet coefficients; SVD reconstruction; wavelet transform; weighted L1 distance; barycentric coordinates; SVD decomposition; one-nearest-neighbour classifier; Brodatz database; low-frequency domain; three-level high-frequency domains; UMD database

Subjects: Image recognition; Algebra; Algebra; Integral transforms; Integral transforms; Computer vision and image processing techniques

References

    1. 1)
      • 31. Demmel, J., Kahan, W.: ‘Accurate singular values of bidiagonal matrices, Society for Industrial and Applied Mathematics’, J. Sci. Stat. Comput., 1990, 11, (5), pp. 873–912.
    2. 2)
      • 19. Dong, Y., Ma, J.: ‘Wavelet-based image texture classification using local energy histograms’, IEEE Signal Process. Lett., 2011, 18, (4), pp. 247–250.
    3. 3)
      • 6. Dong, Y., Ma, J.W.: ‘Statistic contourlet subband characterization for texture image retrievel’. Proc. 8th Int. Conf. Intelligent Computing, Huangshan, China, 2012, pp. 500–507.
    4. 4)
      • 32. Available at http://www.ux.uis.no/tranden/brodatz.html.
    5. 5)
      • 24. Chang, B.-M., Tsai, H.-H., Yen, C.-Y.: ‘SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains’, Eng. Appl. Artif. Intell., 2016, 52, pp. 96–107.
    6. 6)
      • 18. Li, L., Tong, C.S., Choy, S.K.: ‘Texture classification using refined histogram’, IEEE Trans. Image Process., 2010, 19, (5), pp. 1371–1378.
    7. 7)
      • 28. Mandelbrot, B.B.: ‘The fractal geometry of nature’ (W.H. Freeman, San Francisco, CA, 1982).
    8. 8)
      • 16. Pi, M.H., Tong, C.S., Choy, S.K., et al: ‘A fast and effective model for wavelet subband histograms and its application in texture image retrieval’, IEEE Trans. Image Process., 2006, 15, (10), pp. 3078–3088.
    9. 9)
      • 13. Choy, S.K., Tong, C.: ‘Statistical wavelet subband characterization based on generalized Gamma density and its application in texture retrieval’, IEEE Trans. Image Process., 2010, 19, (2), pp. 281–289.
    10. 10)
      • 14. Kim, S.C., Kang, T.J.: ‘Texture classification and segmentation using wavelet packet frame and Gaussian mixture model’, Pattern Recognit., 2007, 40, (4), pp. 1207–1221.
    11. 11)
      • 25. Kennedy, J., Eberhart, R.C.: ‘Particle swarm optimization’. Proc. IEEE Int. Conf. Neural Netw. IV, 1995, pp. 1942–1948.
    12. 12)
      • 7. Dong, Y., Tao, D., Li, X., et al: ‘Texture classification and retrieval using shearlets and linear regression’, IEEE Trans. Cyberen., 2015, 45, (3), pp. 358–369.
    13. 13)
      • 12. Laine, A., Fan, J.: ‘Texture classification by wavelet packet signatures’, IEEE Trans. Pattern Anal. Mach. Intell., 1993, 15, (11), pp. 1186–1191.
    14. 14)
      • 2. Allili, M.S.: ‘Wavelet modeling using finite mixtures of generalized gaussian distributions: application to texture discrimination and retrieval’, IEEE Trans. Image Process., 2012, 21, (4), pp. 1452–1464.
    15. 15)
      • 22. Bryt, O., Elad, M.: ‘Compression of facial images using the K-SVD algorithm’, J. Vis. Commun. Image R, 2008, 19, pp. 270–282.
    16. 16)
      • 8. Mallat, S.G.: ‘A theory for multiresolution signal decomposition: the wavelet representation’, IEEE Trans. Pattern Anal. Mach. Intell., 1989, 11, (7), pp. 674–693.
    17. 17)
      • 5. Dong, Y., Ma, J.: ‘Bayesian texture classification based on contourlet transform and BYY harmony learning of Poisson mixtures’, IEEE Trans. Image Process., 2012, 21, (3), pp. 909–918.
    18. 18)
      • 17. Choy, S.K., Tong, C.S.: ‘Statistical properties of bit-plane probability model and its application in supervised texture classification’, IEEE Trans. Image Process., 2008, 17, (8), pp. 1399–1405.
    19. 19)
      • 11. Randen, T., Husoy, J.H.: ‘Filtering for texture classification: a comparative study’, IEEE Trans. Pattern Anal. Mach. Intell., 1999, 21, (4), pp. 291–310.
    20. 20)
      • 33. Available at http://www.cfar.umd.edu/fer/High-resolution-data-base/hr-database.htm.
    21. 21)
      • 1. Kwitt, R.: ‘Peter meerwald and andreas Uhl, efficient texture image retrieval using copulas in a bayesian framework’, IEEE Trans. Image Process., 2011, 20, (7), pp. 1452–1464.
    22. 22)
      • 26. Poli, R., Kennedy, J., Blackwell, T.: ‘Particle swarm optimization – an overview’, Swarm Intell., 2007, 1, pp. 33–57.
    23. 23)
      • 3. Liu, X., Wang, D.: ‘Texture classification using spectral histograms’, IEEE Trans. Image Process., 2003, 12, (6), pp. 661–670.
    24. 24)
      • 34. Wang, S., Wang, G.: ‘Texture classification by bit-plane multifractal spectrum and bit-plane barycentric coordinates of wavelet coefficients’. ICISME, 2015.
    25. 25)
      • 15. Choy, S.K., Tong, C.S.: ‘Supervised texture classification using characteristic generalized Gaussian density’, J. Math. Imaging Vis., 2007, 29, (1), pp. 35–47.
    26. 26)
      • 10. Mallat, S.: ‘A wavelet tour of signal processing’ (Academic Press, 2008, 3rd edn.).
    27. 27)
      • 27. Cheng, H.D., Guo, Y., Zhang, Y.: ‘A novel Hough transform based on eliminating particle swarm optimization and its applications’, Pattern Recognit., 2009, 42, (9), pp. 1959–1969.
    28. 28)
      • 30. Sudipto, B., Anindya, R.: ‘Linear algebra and matrix analysis for statistics’, Texts in Statistical Science, (Chapman and Hall/CRC, 2014, 1st edn.).
    29. 29)
      • 21. Xu, Y., Ji, H., Fermuller, C.: ‘A projective invariant for textures’. CVPR, 2006, vol. 2, pp. 1932–1939.
    30. 30)
      • 4. Dong, Y., Ma, J.: ‘Texture classification based on contourlet subband clustering’. Proc. 7th Int. Conf. Intelligent Computing, Zhengzhou, China, 2011, pp. 421–426.
    31. 31)
      • 20. Ji, H., Yang, X., Ling, H., et al: ‘Wavelet domain multifractal analysis for static and dynamic texture classification’, IEEE Trans. Image Process., 2013, 22, (1), pp. 286–299.
    32. 32)
      • 9. Daubechies, I.: ‘Ten lectures on wavelets’ (CBMS-NSF Lecture Notes nr.61, SIAM, 1992).
    33. 33)
      • 23. Targhi, A.T., Shademan, A.: ‘Clustering of singular value decomposition of image data with applications to texture classification’, Proc. SPIE, Visual Communications and Image Processing, 2003, vol. 5150, pp. 972–979.
    34. 34)
      • 29. Backes, A.R., Bruno, O.M.: ‘Fractal and multi-scale fractal dimension analysis: a comparative study of Bouligand-Minkowski method’, INFOCOMP, 2008, 7, pp. 74–83.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2016.0875
Loading

Related content

content/journals/10.1049/iet-ipr.2016.0875
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading