Learning non-redundant codebooks for classifying complex objects

ACM International Conference Proceeding Series

Volumn 382, Issue , 2009, Pages

  Zhang, Wei ^a   Surve, Akshat ^b   Fern, Xiaoli ^b   Dietterich, Thomas ^b  

^a HEWLETT PACKARD LABORATORIES   (United States)

^b OREGON STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION DOMAINS; CLASSIFICATION TASKS; CODEBOOKS; COMPLEX OBJECTS; LOW-LEVEL FEATURES; VISUAL OBJECTS;

EDUCATION; INFORMATION RETRIEVAL SYSTEMS; ROBOT LEARNING;

OBJECT RECOGNITION;

EID: 70049116285     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1553374.1553533     Document Type: Conference Paper

References (31)

1. Baker, L. D. & McCallum, A. K. (1998). Distributional clustering of words for text classification. In Proc. SI-GIR conf. Resear. and develo. infor. retriev., pp 96-103.
2. Bekkerman, R., El-yaniv, R., Tishby, N., Winter, Y., Guyon, I., & Elisseeff, A. (2003). Distributional word clusters vs. words for text categorization, J. of Machine Learning Research, Vol 3, pp 1183-1208.
3. Breiman, L. (1996). Bagging predictors. Machine Learn-ing, 24 (2), pp 123-140.
4. Chechik, G. & Tishby, N. (2002). Extracting relevant structures with side information. Proc. Advances in Neural Information Processing Systems, pp 857-864.
5. Csurka, G., Dance, C. R., Fan L., Willamowski, J., & Bray, C. (2004). Visual categorization with bags of keypoints. Euro. Conf. Comput. Vision Workshop, pp 59-74.
6. Cui, Y., Fern, X. Z., & Dy, J. G. (2007). Non-redundant Multi-view Clustering via Orthogonalization. IEEE Int'l Conf. on Data Mining, pp 133-142.
7. Deng, H., Zhang W., Mortensen, E., Dietterich, T. & Shapiro, L. (2007). Principal curvature-based region de-tector for object recognition. Proc. IEEE Conf. Comput. Vision Pattern Recognition, pp 1-8.
8. Dhillon, I., Mallela, S. & Kumar, R. (2003). A divisive information-theoretic feature clustering algorithm for text classification. J. of Machine Learning Re-search, Vol 3, pp 1265-1287.
9. Dietterich, T. G., Lathrop, R. H., & Lozano-Perez, T. (1997). Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, Vol 89, pp 31-71.
10. Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, pp 1895-1923.
11. Dorko, G. & Schmid, C. (2005). Object class recognition using discriminative local features. Technical Report RR-5497, INRIA-Rhone-Alpes.
12. Freund, Y. & Schapire, R. (1996). Experiments with a new boosting algorithm. Proc. Int'l Conf. Machine Learning, pp 148-156.
13. Jain, P., Meka R., & Dhillon I. S. (2008). Simultaneous Unsupervised Learning of Disparate Clusterings. Statis-tical Analysis and Data Mining. Vol 1, pp 195-210.
14. Jurie, F. & Triggs, B. (2005). Creating efficient code-books for visual recognition. Proc. IEEE Int'l Conf. Comput. Vision, Vol 1, pp 604-610.
15. Kadir, T., Zisserman A., & Brady, M. (2004). An affine invariant salient region detector. Proc. Euro. Conf. Comput. Vision, pp 228-241.
16. Kalal, Z., Matas, J., & Mikolajczyk K. (2008). Weighted sampling for large-scale boosting. Proc. Brit. Machine Vision Conf.
17. Larios, N. et al. (2008). Automated insect identification through concatenated histograms of local appearance features. Machine Vis. and App., 19(2), pp 105-123.
18. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision., 2(60), pp 91-110.
19. McCallum, A. K. (2002). MALLET: A machine learning for language toolkit. http://mallet.cs.umass.edu.
20. Mikolajczyk, K., & Schmid, C. (2004). Scale and affine invariant interest point detectors. Int. J. Comput. Vision., Vol 60, pp 63-86.
21. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Van Gool, L. (2005). A comparison of affine region detectors. Int. J. Comput. Vision., Vol 65, pp 43-72.
22. Moosmann, F., Triggs, B. & Jurie, F. (2007). Fast discri-minative visual codebooks using randomized clustering forests. Proc. Advances in Neural Information Processing Systems, pp 985-992.
23. Opelt, A, Pinz A, Fussenegger. M, & Auer P. (2006). Ge-neric Object Recognition with Boosting. IEEE Trans. Pattern Anal. Mach. Intell., 28(3), pp 416-431.
24. Perronnin, F., Dance, C., Csurka, G. & Bressan, M. (2006). Adapted vocabularies for generic visual catego-rization. Proc. Euro. Conf. Comput. Vision, pp 464-475.
25. Quinlan, J. R. (1993). C4.5: Programs for machine learn-ing. Morgan Kaufmann Publishers Inc.
26. Salton, G. & Buckley, C. (1988). Term-weighting ap-proaches in automatic text retrieval. Information Processing & Management, 24(5), pp 513-523.
27. Slonim, N. & Tishby, N. (2001). The power of word clus-ters for text classification. In Proc. Euro. Colloq. Infor-mation Retrieval Research.
28. Slonim, N., Friedman, N., & Tishby, N. (2002). Unsuper-vised document classification using sequential informa-tion maximization. Proc. SIGIR conf. Resear. and deve-lo. infor. retriev., pp 129-136.
29. Viola, P. & Jones, M. (2001). Rapid object detection us-ing a boosted cascade of simple features. Proc. IEEE Conf. Comput. Vision Pattern Recognition, pp 511-518.
30. Winn, J., Criminisi, A. & Minka, T. (2005). Object cate-gorization by learned universal visual dictionary. Proc. IEEE Int'l Conf. Comput. Vision, Vol 2, pp 1800-1807.
31. Yang, L., Jin, R., Sukthankar R., & Jurie, F. (2008). Un-ifying discriminative visual codebook generation with classifier training for object category recognition. Proc. IEEE Conf. Comput. Vision Pattern Recognition, pp 1-8.