Identifying painters from color profiles of skin patches in painting images

IEEE International Conference on Image Processing

Volumn 1, Issue , 2003, Pages 845-848

  Widjaja, Ivo ^a   Leow, Wee Kheng ^a   Wu, Fang Cheng ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b NATIONAL CENTRAL UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ICONOGRAPHY; SUPPORT VECTOR MACHINES (SVM);

COLOR IMAGE PROCESSING; FACE RECOGNITION; GRAPH THEORY; NEURAL NETWORKS; PAINTING; SEMANTICS;

IMAGE ANALYSIS;

EID: 0344235275     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Parramón's Editorial Team, How to Recognize Styles, Barron, New York, 1997.
2. R. Sablatnig, P. Kammerer, and E. Zolda, "Hierarchical classification of paintings using face and brush stroke models," in Proc. of 14th Int. Conf. on Pattern Recognition, 1998, vol. I, pp. 172-174.
3. H. J. van den Herik and E. O. Postma, "Discovering the visual signature of painters," in Future Directions for Intelligent Systems and Information Sciences, pp. 129-147. Springer-Verlag, 2000.
4. B. Cole, The Renaissance Artist at Work: From Pisano to Titian, Harper & Rows, New York, 1983.
5. M. Baxandall, Painting and Experience in Fifteenth-Century Italy, Oxford University Press, 1991.
6. R. H. Bartels, J. C. Beatty, and B. A. Barsky, An Introduction to Splines for Use in Computer Graphics and Geometric Modeling, Morgan Kaufmann, 1998.
7. V. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995.
8. O. Chapelle, P. Haffner, and V. Vapnik, "Support vector machines for histogram-based image classification," IEEE Trans. on Neural Networks, vol. 10, pp. 1055-1064, 1999.
9. C. W. Hsu and C. J. Lin, "A comparison of methods for multi-class support vector machines," IEEE Trans. on Neural Networks, vol. 13, pp. 415-425, 2002.
10. J. Weston and C. Watkins, "Multi-class support vector machines," Tech. Rep. CSD-TR-98-04, Dept. of Computer Science, University of London, UK, 1998.
11. K. Crammer and Y. Singer, "On the algorithmic implementation of multiclass kernel-based vector machines," Journal of Machine Learning Research, 2001.
12. S. Knerr, L. Personnaz, and G. Dreyfus, "Single-layer learning revisited: A stepwise procedure for building and training a neural network," in Neurocomputing: Algorithms, Architectures and Applications, F. Fogelman-Soulié and J. Hérault, Eds., pp. 41-50. Springer-Verlag, 1990.
13. J. Friedman, "Another approach to polychotomous classification," Tech. Rep., Department of Statistics, Stanford University, 1996.
14. T. G. Dietterich and G. Bakiri, "Error-correcting output codes: A general method for improving multiclass inductive learning programs," in Proc. of the 9th AAAI National Conference on Artificial Intelligence, 1991, pp. 572-577.
15. J. Platt, N. Cristianini, and J. Shawe-Taylor, "Large margin DAGs for multiclass classification," in Advances in Neural Information Processing Systems, S. A. Solla, T. K. Leen, and K.-R. Muller, Eds. MIT Press, Cambridge, 2000.
16. U. Kreßel, "Pairwise classification and support vector machines," in Advances in Kernel Methods: Support Vector Learning, B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds., pp. 255-268. MIT Press, Cambridge, 1999.
17. A. R. Smith, "Color gamut transform pairs," in Proc. of the 5th Annual Conf. on Computer Graphics and Interactive Techniques, 1978, pp. 12-19.
18. W. Ostwald, Colour Science, Winsor and Winsor, 1931.
19. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley, 1993.
20. R. S. Berns, Billmeyer and Saltzman's Principles of Color, John Wiley and Sons, 2000.
21. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, "Choosing multiple parameters for support vector machines," Machine Learning, vol. 46, no. 1-3, pp. 131-159, 2002.