Texture Image Retrieval Based on Log-Gabor Features

  • Rodrigo Nava
  • Boris Escalante-Ramírez
  • Gabriel Cristóbal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

Since Daugman found out that the properties of Gabor filters match the early psychophysical features of simple receptive fields of the Human Visual System (HVS), they have been widely used to extract texture information from images for retrieval of image data. However, Gabor filters have not zero mean, which produces a non-uniform coverage of the Fourier domain. This distortion causes fairly poor pattern retrieval accuracy. To address this issue, we propose a simple yet efficient image retrieval approach based on a novel log-Gabor filter scheme. We make emphasis on the filter design to preserve the relationship with receptive fields and take advantage of their strong orientation selectivity. We provide an experimental evaluation of both Gabor and log-Gabor features using two metrics, the Kullback-Leibler (D KL ) and the Jensen-Shannon divergence (D JS ). The experiments with the USC-SIPI database confirm that our proposal shows better retrieval performance than the classic Gabor features. 3

Keywords

Gabor filters Image retrieval Jensen-Shannon divergence Log-Gabor filters Texture analysis 

References

  1. 1.
    Xing-yuan, W., Zhi-feng, C., Jiao-jiao, Y.: An effective method for color image retrieval based on texture. Computer Standards & Interfaces 34(1), 31–35 (2012)CrossRefGoogle Scholar
  2. 2.
    Huang, P.W., Dai, S.K.: Image retrieval by texture similarity. Pattern Recognition 36(3), 665–679 (2003)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Jie, Y., Qiang, Z., Liang, Z., Wuhan, C.Y.: Research on texture images retrieval based on the Gabor wavelet transform. In: International Conference on Information Engineering, ICIE 2009, vol. 1, pp. 79–82 (2009)Google Scholar
  4. 4.
    ElAlami, M.E.: A novel image retrieval model based on the most relevant features. Knowledge-Based Systems 24(1), 23–32 (2011)CrossRefGoogle Scholar
  5. 5.
    Zhang, G., Ma, Z.M.: Texture feature extraction and description using Gabor wavelet in content-based medical image retrieval. In: International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2007, vol. 1, pp. 169–173 (2007)Google Scholar
  6. 6.
    Turner, M.R.: Texture discrimination by Gabor functions. Biological Cybernetics 55, 71–82 (1986)Google Scholar
  7. 7.
    Nava, R., Cristóbal, G., Escalante-Ramírez, B.: A comprehensive study of texture analysis based on local binary patterns. In: Optics, Photonics, and Digital Technologies for Multimedia Applications II 8436-1, 84360E. SPIE (2012)Google Scholar
  8. 8.
    Randen, T., Husøy, J.H.: Filtering for texture classification: A comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21, 291–310 (1999)CrossRefGoogle Scholar
  9. 9.
    Nava, R., Escalante-Ramírez, B., Cristóbal, G.: A comparison study of Gabor and log-Gabor wavelets for texture segmentation. In: 7th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 189–194 (2011)Google Scholar
  10. 10.
    Kong, A.W.-K.: An Analysis of Gabor Detection. In: Kamel, M., Campilho, A. (eds.) ICIAR 2009. LNCS, vol. 5627, pp. 64–72. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2, 1160–1169 (1985)CrossRefGoogle Scholar
  12. 12.
    Hubel, D.H., Wiesel, T.N.: Brain and Visual Perception: The Story of a 25-year Collaboration. Oxford University Press, Oxford (2005)Google Scholar
  13. 13.
    Gabor, D.: Theory of communication. J. Inst. Elec. Eng. (London) 93(III), 429–457 (1946)Google Scholar
  14. 14.
    Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8), 837–842 (1996)CrossRefGoogle Scholar
  15. 15.
    Sastry, C.S., Ravindranath, M., Pujari, A.K., Deekshatulu, B.: A modified Gabor function for content based image retrieval. Pattern Recognition Letters 28(2), 293–300 (2007)CrossRefGoogle Scholar
  16. 16.
    Brodatz, P.: USC-SIPI (2012), http://sipi.usc.edu/database/database.php?volume=rotate (Online accessed March 1, 2012)
  17. 17.
    Redondo, R., Šroubek, F., Fischer, S., Cristóbal, G.: Multifocus image fusion using the log-Gabor transform and a multisize windows technique. Information Fusion 10(2), 163–171 (2009)CrossRefGoogle Scholar
  18. 18.
    Bovik, A.C., Clark, M., Geisler, W.S.: Multichannel texture analysis using localized spatial filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 55–73 (1990)CrossRefGoogle Scholar
  19. 19.
    Clausi, D.A., Jernigan, M.E.: Designing Gabor filters for optimal texture separability. Pattern Recognition 33(11), 1835–1849 (2000)CrossRefGoogle Scholar
  20. 20.
    Field, D.J.: Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4(12), 2379–2394 (1987)CrossRefGoogle Scholar
  21. 21.
    Gross, M., Koch, R.: Visualization of multidimensional shape and texture features in laser range data using complex-valued Gabor wavelets. IEEE Transactions on Visualization and Computer Graphics 1(1), 44–59 (1995)CrossRefGoogle Scholar
  22. 22.
    Lin, J.: Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory 37(1), 145–151 (1991)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rodrigo Nava
    • 1
  • Boris Escalante-Ramírez
    • 2
  • Gabriel Cristóbal
    • 3
  1. 1.Posgrado en Ciencia e Ingeniería de la ComputaciónUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  2. 2.Departamento de Procesamiento de Señales, Facultad de IngenieríaUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  3. 3.Instituto de Óptica, Spanish National Research Council (CSIC)MadridSpain

Personalised recommendations