Advertisement

Texture Similarity Measure Using Kullback-Leibler Divergence between Gamma Distributions

  • John Reidar Mathiassen
  • Amund Skavhaug
  • Ketil Bø
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2352)

Abstract

We propose a texture similarity measure based on the Kullback-Leibler divergence between gamma distributions (KLGamma). We conjecture that the spatially smoothed Gabor filter magnitude responses of some classes of visually homogeneous stochastic textures are gamma distributed. Classification experiments with disjoint test and training images, show that the KLGamma measure performs better than other parametric measures. It approaches, and under some conditions exceeds, the classification performance of the best non-parametric measures based on binned marginal histograms, although it has a computational cost at least an order of magnitude less. Thus, the KLGamma measure is well suited for use in real-time image segmentation algorithms and time-critical texture classification and retrieval from large databases.

Keywords

Similarity Measure Gamma Distribution Gabor Filter Magnitude Response Gabor Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tai Sing Lee, Image Representation Using 2D Gabor Wavelets, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 18, No. 10, October 1996Google Scholar
  2. 2.
    David A. Clausi, M. Ed Jernigan, Designing Gabor filters for optimal texture separability, Pattern Recognition 33 (2000) 1835–1849CrossRefGoogle Scholar
  3. 3.
    W.D. Penny, KL-Divergences of Normal, Gamma, Direchlet and Wishart densities, Wellcome Department of Cognitive Neurology, University College London, March 30, 2001Google Scholar
  4. 4.
    S. Marcelja, Mathematical Description of the Responses of Simple Cortical Cells, J. Optical Soc. Am., vol. 70, pp. 1297–1300, 1980MathSciNetGoogle Scholar
  5. 5.
    J.G. Daugman, Two-dimensional Analysis of Cortical Receptive Field Profile, Vision Research, vol. 20, pp. 847–856, 1980CrossRefGoogle Scholar
  6. 6.
    J.G. Daugman, Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters, J. Optical Soc. Am., vol. 2, no. 7, pp. 1160–1169, 1985CrossRefGoogle Scholar
  7. 7.
    D.A. Pollen and S.F. Ronner, Phase Relationship Between Adjacent Simple Cells in the Visual Cortex, Science, vol. 212, pp. 1409–1411, 1981CrossRefGoogle Scholar
  8. 8.
    J.H. van Hateren and A. van der Schaf, Independent Component Filters of Natural Images Compared with Simple Cells in Primary Visual Cortex, Proc.R.Soc.Lond.B 265:359–366, 1998CrossRefGoogle Scholar
  9. 9.
    Ronald E. Walpole and Raymond H. Myers, Probability and Statistics for Scientists and Engineers 5th Edition, Prentice Hall, 1993Google Scholar
  10. 10.
    C.T.J. Dodson and J. Scharcanski, Information Geometry For Stochastic Refinement of Database Search and Retrieval Algorithms, 2001Google Scholar
  11. 11.
    A.C. Bovik, M. Clark, W.S. Geisler, Multichannel Texture Analysis Using Localized Spatial Filters, IEEE Trans. Pattern Anal. Machine Intell. 12(1) (1990) 55–73CrossRefGoogle Scholar
  12. 12.
    Nuno Vasconcelos and Andrew Lippman, A Unifying View of Image Similarity, MIT Media LabGoogle Scholar
  13. 13.
    K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, 1990Google Scholar
  14. 14.
    B.S. Manjunath and W.Y. Ma, Texture Features for Browsing and Retrieval of Image Data, IEEE Trans. Pattern Anal. Machine Intell. 18(8) (1996) 837–842CrossRefGoogle Scholar
  15. 15.
    Jan Puzicha, Joachim M. Buhmann, Yossi Rubner and Carlo Tomasi, Empirical Evaluation of Dissimilarity Measures for Color and Texture, Proceedings of the Internation Conference on Computer Vision (ICCV’99), 1165–1173, 1999Google Scholar
  16. 16.
    Jan Puzicha, Thomas Hofmann and Joachim M. Buhmann, Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval, Proc. of the IEEE Int. Conf. on Computer Vision and Pattern Recognition, San Juan, 1997Google Scholar
  17. 17.
    Thomas Hofmann, Jan Puzicha and Joachim M. Buhmann, Deterministic Annealing for Unsupervised Texture Segmentation, Proc. of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR’97)Google Scholar
  18. 18.
    Richard J. Larsen and Morris L. Marx, An Introduction to Mathematical Statistics and Its Applications 3’rd Edition, Prentice Hall, 2001Google Scholar
  19. 19.
    W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.V.P. Flannery, Numerical Recipes in C, Cambridge, 1992Google Scholar
  20. 20.
    G. R. Cross and A. K. Jain. Markov Random Field Texture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 5/1, 1983Google Scholar
  21. 21.
    Chun-Shien Lu and Pau-Choo Chung. Wold Features for Unsupervised Texture Segmentation. Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC, 1998Google Scholar
  22. 22.
    K. I. Laws. Rapid texture identification. In Proc. of the SPIE Conference on Image Processing for Missile Guidance, pages 376–380, 1980Google Scholar
  23. 23.
    Timo Ojala and Matti Pietikäinen, Unsupervised texture segmentation using feature distributions. Pattern Recognition, Vol. 32, pp. 477–486, 1999CrossRefGoogle Scholar
  24. 24.
    Trygve Randen and John Håkon Husøy. Filtering for Texture Classification: A Comparative Study. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, Apr 1999. Web: http://www.ux.his.no/~tranden/

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • John Reidar Mathiassen
    • 1
  • Amund Skavhaug
    • 2
  • Ketil Bø
    • 3
  1. 1.SINTEF Fisheries and AquacultureTrondheimNorway
  2. 2.Department of Engineering Cybernetics (ITK)NTNUTrondheimNorway
  3. 3.Department of Computer and Information Science (IDI)NTNUTrondheimNorway

Personalised recommendations