Class-specific material categorisation

Proceedings of the IEEE International Conference on Computer Vision

Volumn II, Issue , 2005, Pages 1597-1604

  Caputo, Barbara ^a   Hayman, Eric ^a   Mallikarjuna, P ^a  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; IMAGE ANALYSIS; OBJECT RECOGNITION; PARAMETER ESTIMATION; PERSONNEL TRAINING; SET THEORY; STRATEGIC MATERIALS; TEXTURES;

APPEARANCE-BASED STRATEGIES; CLASS-SPECIFIC MATERIAL CATEGORIZATION; TEST SETS; TEXTURE DESCRIPTORS;

COMPUTER VISION;

EID: 33745899935     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2005.54     Document Type: Conference Paper

References (26)

1. P. M. Baggenstoss and H. Niemann. A Theoretically Optimal Probabilistic Classifier Using Class-specific Features ICPR2002
2. G. H. Bakir, L. Bottou, J. Weston. Breaking SVM complexity with cross-training. NIPS2004
3. B. Caputo and H. Niemann. To each according to its need: kernel class specific classifiers. ICPR2002.
4. S. Belongie, C. Fowlkes, F. Chung, and J. Malik. Spectral partitioning with indefinite kernels using the nyström extension. ECCV2002.
5. P.Brodatz. Textures. Dover, 1966.
6. C. Burges. Geometry and invariance in kernel based methods. Advances in Kernel Methods - Support Vector Learning, 1998.
7. N. Cristianini and J. S. Taylor. An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, 2000.
8. O. Cula and K. Dana. Compact representation of bidirectional texture functions. CVR2001.
9. O. Cula and K. Dana. Image-based skin analysis. Texture Workshop, pages 35-40, 2002.
10. K. Dana, B. van Ginneken, S. Nayar, and J. Koenderink. Reflectance and texture of real-world surfaces. TG, 18(1):1-34, 1999.
11. H. Ganster, P. Pinz, R. Rohrer, E. Wildling, M. Binder, H. Kittler. Automated melanoma recognition. IEEE MI, 20(3):233-239, 2001.
12. E. Hayman, B. Caputo, M. Fritz, J.-O. Eklundh. On the significance of real-world conditions for material classification. ECCV2004.
13. C-W Hsu, C-J Lin. A comparison of methods for multiclass support vector machines. IEEE TNN, 13 (2), 415-425, 2002.
14. S. Lazebnik, C. Schmid, J. Ponce. Affine-invariant local descriptors and neighbourhood statistics for texture recognition. ICCV2003.
15. T. Leung, J. Malik. Representing and recognizing the visual appearance of materials using three-dimensional textons. IJCV, 43(1):29-44, 2001.
16. T. Mäenpää and M. Pietikäinen. Multi-scale binary patterns for texture analysis. SCIA2003.
17. P. Mallikarjuna, M. Fritz, A. Tavakoli Targhi, E. Hayman, B. Caputo, and J.-O. Eklundh The KTH-TIPS and KTH-TIPS2 databases. www.nada.kth.se/cvap/ databases/kth-tips.
18. M. Nilsback and B. Caputo. Cue integration through discriminative accumulation. CVPR2004.
19. T. Ojala, M. Pietikäinen, and D. Harwood. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29(1):51-59, Jan 1996.
20. T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans on PAMI, 24(7):971-987, July 2002.
21. M. Pietikainen, T. Nurmela, T. Maenpaa, M. Turtinen. View-based recognition of real-world textures. PR, 37(2):313-323, 2004.
22. L. Ruiz, A. Fdez-Sarra, and J. Recio. Texture feature extraction for classification of remote sensing data using wavelet decomposition: A comparative study. 20th ISPRS Congress, 2004.
23. M. Shi and G. Healey. Hyperspectral texture recognition using a multiscale opponent representation. IEEE Trans. Geoscience and Remote Sensing, 41(5): 1090-1095, May 2003.
24. M. Varma and A. Zisserman. Classifying images of materials: Achieving viewpoint and illumination independence. ECCV2002.
25. M. Varma and A. Zisserman. Texture classification: are filter banks necessary? CVPR2003.
26. C. Wallraven, B. Caputo, A. Graf. Recognition with local features: the kernel recipe. ICCV2003.