A statistical approach to texture classification from single images

International Journal of Computer Vision

Volumn 62, Issue 1-2, 2005, Pages 61-81

  Varma, Manik ^a   Zisserman, Andrew ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

3D textures; Filter banks; Material classification; Rotation invariance; Textons

Indexed keywords

ALGORITHMS; IMAGE ANALYSIS; IMAGING TECHNIQUES; LEARNING SYSTEMS; LIGHTING; PATTERN RECOGNITION; STATISTICAL METHODS;

3D TEXTURES; FILTER BANKS; MATERIAL CLASSIFICATION; ROTATION INVARIANCE; TEXTRONS;

TEXTURES;

EID: 8644258401     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:VISI.0000046589.39864.ee     Document Type: Article

References (33)

1. Brodatz, P. 1966. Textures: A Photographic Album for Artists & Designers. Dover: New York.
2. Chantler, M.J., McGunnigle, G., Penirschke, A., and Petrou, M. 2002a. Estimating lighting direction and classifying textures. In Proceedings of the 13th British Machine Vision Conference. Cardiff, pp. 737-746.
3. Chantler, M.J., McGunnigle, G., and Wu, J. 2000. Surface rotation invariant texture classification using photometric stereo and surface magnitude spectra, In Proceedings of the 11th British Machine Vision Conference. Bristol, pp. 486-495.
4. Chantler, M.J., Schmidt, M., Petrou, M., and McGunnigle, G. 2002b. The effect of illuminant rotation on texture filters: Lissajous's ellipses. In Proceedings of the 7th European Conference on Computer Vision. Copenhagen, Denmark, pp. 289-303.
5. Cristianini, N. and Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines. Cambridge University Press.
6. Cula, O.G. and Dana, K.I. 2004.3D Texture recognition using bidirectional feature histograms. International Journal of Computer Vision, 59(1).
7. Dana, K.J. and Nayar, S. 1998. Histogram model for 3d textures. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 618-624.
8. Dana, K.J. and Nayar, S. 1999. Correlation model for 3D texture. In: Proceedings of the International Conference on Computer Vision, pp.1061-1067.
9. Dana, K.J., van Ginneken, B., Nayar, S.K., and Koenderink, J.J. 1999. Reflectance and texture of real world surfaces. ACM Transactions on Graphics, 18(1): 1-34.
10. Duda, R.O., Hart, P.E., and Stork, D.G. 2001. Pattern Classification. John Wiley and Sons, 2nd edition.
11. Fowlkes, C., Martin, D., Ren, X., and Malik, J. 2002. Detecting and localizing boundaries in natural images. Technical report, University of California at Berkeley.
12. Funt, B., Barnard, K., and Martin, L. 1998. Is colour constancy good enough?. In Proceedings of the European Conference on Computer Vision. Springer-Verlag, pp. 445-459.
13. Gates, G.W. 1972. The reduced nearest neighbour rule. IEEE Transactions on Information Theory, 18(3):431-433.
14. Hayman, E., Caputo, B., Fritz, M., and Eklundh, J. 2004. On the significance of real-world conditions for material classification. In Proceedings of the European Conference on Computer Vision to appear.
15. Kaufman, L. and Rousseeuw, P.J. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, NY, USA.
16. Kim, K.I., Jung, K., Park, S.H., and Kim, H.J. 2002. Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(11):1542-1550.
17. Konishi, S. and Yuille, A.L. 2000. Statistical cues for domain specific image segmentation with performance analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 125-132.
18. Lazebnik, S., Schmid, C., and Ponce, J. 2003a. Affine-invariant local descriptors and neighborhood statistics for texture recognition. In Proceedings of the International Conference on Computer Vision, pp. 649-655.
19. Lazebnik, S., Schmid, C., and Ponce, J. 2003b. A spars: texture representation using affine-invariant regions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2. pp. 319-324.
20. Leung, T. and Malik, J. 2001. Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 43(1):29-44.
21. Lindeberg, T. and Gårding, J. 1994. Shape-adapted smoothing in estimation of 3-D depth cues from affine distortions of local 2-D brightness structure. In Proceedings of the 3rd European Conference on Computer Vision. Stockholm, Sweden, pp. 389-400.
22. Malik, J., Belongie, S., Leung, T., and Shi, J. 2001. Contour and texture analysis for image segmentation. International Journal of Computer Vision, 43(1):7-27.
23. Penirschke, A., Chantler, M.J., and Petrou, M. 2002. Illuminant rotation invariant classification of 3D surface textures using lissajous's ellipses, In Proceedings of the 2nd International Workshop on Texture Analysis and Synthesis. Copenhagen, Denmark, pp. 103-108.
24. Perona, P. 1992. Steerable-scalable kernels for edge detection and junction analysis. In European Conference on Computer Vision, pp. 3-18.
25. Press, W., Teukolsky, S., Vetterling, W., and Flannery, B. 1992. Numerical Recipes in C (2nd ed.). Cambridge University Press.
26. Schaffalitzky, F. and Zisserman, A. 2001. Viewpoint invariant texture matching and wide baseline stereo. In Proceedings of the 8th International Conference on Computer Vision. Vancouver, Canada, pp. 636-643.
27. Schmid, C. 2001. Constructing models for content-based image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 39-45.
28. Scholkopf, B. and Smola, A. 2002. Learning with Kernels. MIT Press.
29. Suen, P. and Healey, G. 2000. The analysis and reconstruction of real-world textures in three dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(5):491-503.
30. Tipping, M. 2001. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211-244.
31. Toussaint, G. 2002. Proximity graphs for nearest neighbor decision rules: Recent progress. In Interface 2002, 34th Symposium on Computing and Statistics.
32. Varma, M. and Zisserman, A. 2002. Classifying images of materials: Achieving viewpoint and illumination independence. In Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, vol. 3, Springer-Verlag, pp. 255-271.
33. Varma, M. and Zisserman, A. 2004. On unifying statistical texture classification frameworks. Image and Vision Computing (to appear).