Skip to main content

Homomorphic Normalization-Based Descriptors for Texture Classification

Abstract

Illumination variation is an essential trait in texture analysis, since the same texture can be surrounded with different illuminations, which can greatly affect the classification rate. This paper introduces a new approach to extract the texture features applying illumination normalization-based texture descriptors. The prime objective is to enhance the classification accuracy by normalizing the illumination of colour textures. Normalization of illumination is achieved by applying homomorphic filter. For feature extraction, two relevant approaches grey-level co-occurrence matrix (GLCM) and Laws’ mask are utilized. Experiments are conducted for normalized co-occurrence and Laws’ filter for colour images. Classification rates of the traditional GLCM and Laws’ mask descriptors are included for baseline comparison. The effectiveness of the introduced techniques is assessed on three benchmark texture datasets, i.e. STex, VisTex, and ALOT. A k-nearest neighbour (k-NN) classifier is utilized to perform texture classification. Results show that the proposed approach has achieved higher classification rates and outperformed existing methods.

This is a preview of subscription content, access via your institution.

References

  1. Tan, T.S.C.; Kittler, J.: Colour texture analysis using colour histogram. IEEE Proc. Vis. Image Signal Proces 141(6), 403–412 (1994)

    Article  Google Scholar 

  2. Kyllonen, J.; Pietikainen, M.: 5-1 Visual inspection of parquet slabs by combining color and texture. In: Proceedings of IAPR Workshop on Machine Vision Applications (MVA’00). November 28–30, Tokyo, Japan. pp. 187–192 (2000). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.4457

  3. Drimbarean, A.; Whelan, P.F.: Experiments in colour texture analysis. Patt. Recogn. Lett. 22, 1161–1167 (2001). https://doi.org/10.1016/S0167-8655(01)00058-7

    Article  MATH  Google Scholar 

  4. Palm, C.; Lehmann, T.M.: Classification of colour textures by Gabor filtering. Mach. GRAP. Vis. 11(2/3), 195–219 (2002). https://doi.org/10.1109/34.41384

    Google Scholar 

  5. Palm, C.: Colour texture classification by integrative co-occurrence matrices. Patt. Recogn. 37, 965–976 (2004). https://doi.org/10.1016/j.patcog.2003.09.010

    Article  Google Scholar 

  6. Arivazhagan, S.; Ganesan, L.; Angayarkanni, V.: Color texture classification using WSFs and WCFs. Multi. Cyperscape J.-Special Iss. Mult. Data Proc. Comp. 3(4), 297–302 (2005). https://doi.org/10.1109/ICCIMA.2005.46

  7. Arivazhagan, S.; Ganesan, L.; Joyson, C.T.: Color texture image classification using wavelet texture spectral features. Int. J. Biomed. Eng. Consum. Health Inform. 3(1), 21–27 (2011). ISSN: 0973-6727.

  8. Dey, M.; Raman, B.; Verma, M.: A novel colour-and texture-based image retrieval technique using multi-resolution local extrema peak valley pattern and RGB colour histogram. Pattern Anal. Appl. 19(4), 1159–1179 (2016)

    MathSciNet  Article  Google Scholar 

  9. Haralick, R. M.: Statistical and structural approaches to texture. In: Proceedings of IEEE (Vol. 67, pp. 786–804). (1979). https://doi.org/10.1109/proc.1979.11328

  10. Laws, K.I.: Texture energy measures. In: Image Und. Workshop. (1979).

  11. Ojala, T.; Pietikainen, M.; Maenpaa, T.: Multi resolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002). https://doi.org/10.1109/TPAMI.2002.1017623

  12. Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Trans. Image. Process. 4(11), 1549–1560 (1995). https://doi.org/10.1109/83.469936

  13. Manjunath, B.S.; Ma, W.Y.: Texture features for browsing and retrieval of large image data. IEEE Trans. Patt. Anal. Machine Intell. 18(8), 837–849 (1996). https://doi.org/10.1109/34.531803

  14. Arivazhagan, S.; Ganesan, L.: Texture classification using wavelet transform. Patt. Recogn. Lett. 24(9–10), 1513–1521 (2003). https://doi.org/10.1016/S0167-8655(02)00390-2

  15. de Siqueira, F.R.; Schwartz, W.R.; Pedrini, H.: Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing 120, 336–345 (2013). https://doi.org/10.1016/j.neucom.2012.09.042

  16. Yadav, A.R.; Anand, R.S.; Dewal, M.L.; Gupta, S.: Multiresolution local binary pattern variants based texture feature extraction technique for efficient classification of microscopic images of hard wood species. App. Soft Comp. 32, 101–112 (2015). https://doi.org/10.1016/j.asoc.2015.03.039

  17. Yadav, A.R.; Anand, R.S.; Dewal, M.L.; Gupta, S.: Gaussian image pyramid based texture features for classification of microscopic images of hardwood species. Optik. 126(24) 5570–5578 (2015). https://doi.org/10.1016/j.ijleo.2015.09.030.

  18. Dash, S.; Jena, U.R.: Texture classification using steerable pyramid based Laws’ masks. J. Elect. Syst. Inform. Tech. 4, 185–197 (2017). https://doi.org/10.1016/j.jesit.2016.10.001

  19. Du, S.; Ward, R.: Wavelet-based illumination normalization for face recognition. In: IEEE International Conference on Image Processing (ICIP 2005), Vol. 2, (2005). https://doi.org/10.1109/ICIP.2005.1530215

  20. Jobson, D.J.; Rahman, Z.; Woodell, G.A.: A multiscaleretinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image. Proc. 6(7), 965–976 (1997). https://doi.org/10.1109/83.597272

    Article  Google Scholar 

  21. Delac, K.; Grgic, M.; Kos, T.: Sub-image homomorphic filtering technique for improving facial identification under difficult illumination conditions. In: International Conference System, Signals and Image Processing pp. 95–98 (2006). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.118.1579&rep=rep1&type=pdf

  22. Wang, W.; Song, J.; Yang, Z.; Chi, Z.: Wavelet-based illumination compensation for face recognition using eigenface method. In: Proceedings 6th World Congress Intelligent Control and Automation, Dalian, China (2006). https://doi.org/10.1109/WCICA.2006.1714031

  23. Chen, W.; Joo Er, M.; Wu, S.: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans. Syst. Man Cybern. 36(2), 458–466 (2006). https://doi.org/10.1109/TSMCB.2005.857353

    Article  Google Scholar 

  24. Xie, X.; Lam K-N.: An efficient illumination normalization method for face recognition. Patt. Recg. Lett. 27, 609–617 (2006). https://doi.org/10.1016/j.paterc.2005.09.026

  25. Emadi, M.; Khalid, M.; Yusof, R.; Navabifar, F.: Illumination normalization using 2D Wavelet. Procedia Eng. 41, 854–859 (2012). https://doi.org/10.1016/j.proeng.2012.07.254

    Article  Google Scholar 

  26. Tan, X.; Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Proc. 19(6), 1635–1650 (2010). https://doi.org/10.1109/TIP.2010.2042645

    MathSciNet  Article  MATH  Google Scholar 

  27. Fan, C.N.; Zhang, F.Y.: Homomorphic filtering based illumination normalization method for face recognition. Patt. Recog. Lett. 32, 1468–1479 (2011). https://doi.org/10.1016/j.patrec.2011.03.023

    Article  Google Scholar 

  28. Maenpaa, T.; Pietikainen, M.: Classification with color and texture: jointly or separately? Patt. Recogn. 37(8), 1629–1640 (2004). https://doi.org/10.1016/j.patcog.2003.11.011

    Article  Google Scholar 

  29. Burghouts, G.J.; Geusebroek, J.M.: Material-specific adaption of colour invariant features. Patt. Recgon. Lett. 30, 306–313 (2009). https://doi.org/10.1016/j.patrec.2008.10.005

    Article  Google Scholar 

  30. Vacha, P.; Haindl, M.: Texture recognition using robust Markovian features. In: International Workshop on Computational Intelligence for Multimedia Understanding. Springer, Berlin, pp. 126–137 (2012). https://doi.org/10.1007/978-3-642-32436-9-11

  31. Kononenko, I.; Kukar, M.: Machine learning and data mining: introduction to principles and algorithms. Horwood Publishing Ltd, Chichester (2007)

    Book  MATH  Google Scholar 

  32. Everitt, B.S.; Landau, S.; Leese M.; Stahl, D.: Miscellaneous clustering methods. In Cluster Analysis. Wiley Series in Probability and Statistics. Wiley, New York, pp. 215–255 (2011). https://doi.org/10.1002/9780470977811.ch8

  33. Hastie, T.; Tibshirani R.; Friedman, J.: The elements of statistical learning. Springer series in statistics, New York, Vol. 1, pp. 241–249, (2009). https://doi.org/10.1007/978-0-387-84858

  34. Department of Computer Sciences, U. S., Salzburg texture image database (STex), http://www.wavelab.at/sources/STex/.

  35. VisTex, Texture dataset, http://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html.

  36. Amsterdam library of textures (ALOT), http://www.science.uva.nl/~mark/ALOT.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manas Ranjan Senapati.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dash, S., Jena, U.R. & Senapati, M.R. Homomorphic Normalization-Based Descriptors for Texture Classification. Arab J Sci Eng 43, 4303–4313 (2018). https://doi.org/10.1007/s13369-017-2961-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-017-2961-9

Keywords

  • Texture
  • Colour
  • Laws’ mask
  • GLCM
  • Homomorphic filter
  • Classification