Spectral error correcting output codes for efficient multiclass recognition

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2009, Pages 1111-1118

  Zhang, Xiao ^a   Liang, Lin ^b   Shum, Heung Yeung ^c  

^a TSINGHUA UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

^c MICROSOFT   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFIERS; CLASSIFICATION ACCURACY; CODE-WORDS; CODING EFFICIENCY; EIGENVECTORS; ERROR CORRECTING OUTPUT CODE; GRAPH LAPLACIAN; MULTI-CLASS; MULTI-CLASS PROBLEMS; MULTICLASS RECOGNITION; NP-HARD PROBLEM; SPECTRAL ANALYSIS;

COMPUTER VISION; HAMMING DISTANCE; SPECTRUM ANALYSIS; SPECTRUM ANALYZERS;

CLASSIFIERS;

EID: 77953209151     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2009.5459355     Document Type: Conference Paper

References (24)

1. E. L. Allwein and R. E. Schapire. Reducing multiclass to binary: A unifying approach for margin classifiers. Joural of Machine Learning Research, 1:113-141, 2000.
2. V. Athitsos, A. Stefan, Q. Yuan, and S. Sclaroff. Classmap: Efficient multiclass recognition via embeddings. In ICCV, pages 1-8, Oct. 2007.
3. R. C. Bose and D. K. Ray-Chaudhuri. On a class of error-correcting binary group codes. Information and Control, 3:68-79, 1960.
4. K. Crammer and Y. Singer. On the learnability and design of output codes for multiclass problems. Machine Learning, 47(2-3):201-233, 2002.
5. S. Escalera, D. M. J. Tax, O. Pujol, P. Radeva, and R. P.W. Duin. Subclass problem-dependent design for error-correcting output codes. IEEE Trans. Pattern Anal. Mach. Intell., 30(6):1041-1054, 2008.
6. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. PAMI, 28(2):337-374, 2000.
7. N. García-Pedrajas and C. Fyfe. Evolving output codes for multiclass problems. IEEE Trans. Evolutionary Computation, 12(1):93-106, 2008.
8. R. Ghaderi and T. Windeatt. Circular ecoc: A theoretical and experimental analysis. In ICPR, pages 2203-2206, 2000.
9. G. Griffin and D. Perona. Learning and using taxonomies for fast visual categorization. In CVPR, 2008.
10. T. Hastie and R. Tibshirani. Classification by pairwise coupling. In NIPS, 1997.
11. A. C. Lorena and A. C. P. L. F. de Carvalho. Minimum spanning trees in hierarchical multiclass support vector machines generation. In IEA/AIE, pages 422-431, 2005.
12. M. Nilsback and A. Zisserman. Automated flower classification over a large number of classes. In ICVGIP, pages 722-729, 2008.
13. D. Nistér and H. Stewénius. Scalable recognition with a vocabulary tree. In CVPR (2), pages 2161-2168, 2006.
14. T. Ojala, M. Pietikäinen, and D. Harwood. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1):51-59, 1996. (Pubitemid 126402562)
15. P. J. Phillips, P. J. Flynn, W. T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. J. Worek. Overview of the face recognition grand challenge. In CVPR (1), pages 947-954, 2005.
16. O. Pujol, S. Escalera, and P. Radeva. An incremental node embedding technique for error correcting output codes. Pattern Recognition, 41(2):713-725, 2008. (Pubitemid 47516589)
17. O. Pujol and P. Radeva. Discriminant ecoc: A heuristic method for application dependent design of error correcting output codes. PAMI, 28(6):1007-1012, 2006.
18. R. M. Rifkin and A. Klautau. In defense of one-vs-all classification. Journal of Machine Learning Research, 5:101-141, 2004.
19. R. Schapir and Y. Singer. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263-286, 1995.
20. R. Schapir and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336, 1999. (Pubitemid 32210620)
21. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22(8):888-905, 2000.
22. U. v. Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17(4):395-416, 2007.
23. V. Vapnik. The nature of statistical learning theory. Springer-Verlag, New York, 1995.
24. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, 2008.