Efficient learning of relational object class models

International Journal of Computer Vision

Volumn 77, Issue 1-3, 2008, Pages 175-198

  Bar Hillel, Aharon ^a   Weinshall, Daphna ^b  

^a INTEL CORPORATION   (Israel)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

Boosting; Generative models; Object class recognition; Object localization; Weakly supervised learning

Indexed keywords

BAYESIAN NETWORKS; COMPUTATIONAL COMPLEXITY; IMAGE SEGMENTATION; MATHEMATICAL MODELS; PARAMETER ESTIMATION; SUPERVISED LEARNING;

GENERATIVE MODELS; OBJECT CLASS RECOGNITION; OBJECT LOCALIZATION; WEAKLY SUPERVISED LEARNING;

OBJECT RECOGNITION;

EID: 39749094395     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-007-0091-7     Document Type: Article

References (38)

1. Agarwal, S., Awan, A., & Roth, D. (2004). Learning to detect objects in images via a sparse, part based representation. Pattern Analysis and Machine Intelligence, 20(11), 1475-1490.
2. Agarwal, S., & Roth, D. (2002). Learning a sparse representation for object detection. In ECCV (pp. 113-130).
3. Bar-Hillel, A., Hertz, T., & Weinshall, D. (2005a). Efficient learning of relational object class models. In ICCV.
4. Bar-Hillel, A., Hertz, T., & Weinshall, D. (2005b). Object class recognition by boosting a part based model. In CVPR. Los Alamitos: IEEE Computer Society
5. Borenstein, E., Sharon, E., & Ullman, S. (2004). Combining top-down and bottom-up segmentation. In IEEE workshop on perceptual organization in computer vision (CVPR).
6. Chan, A. B., Vasconcelos, N., & Moreno, P. J. (2004). A family of probabilistic kernels based on information divergence.
7. Csurka, G., Bray, C., Dance, C., & Fan, L. (2004). Visual categorization with bags of keypoints. In ECCV.
8. Dorkó, G., & Schmid, C. (2005, submitted). Object class recognition using discriminative local features. IEEE Transactions on Pattern Analysis and Machine Intelligence.
9. Everingham, M. R., Zisserman, A., Williams, C. K. I., & Van Gool, L. et al. (2006). The 2005 pascal visual object classes challenge. In J. Quinonero-Candela, I. Dagan, B. Magnini, & F. d'Alche-Buc (Eds.), LNAI: Vol. 3944. Machine learning challenges. Evaluating predictive uncertainty, visual object classification, and recognising textual entailment (pp. 117-176).
10. Fei-Fei, L., Fergus, R., & Perona, P. (2003). A bayesian approach to unsupervised one shot learning of object catgories. In ICCV.
11. Feltzenswalb, P., & Huttenlocher, D. (2005). Pictorial structures for object recognition. International Journal of Computer Vision, 61, 55-79.
12. Fergus, R., Perona, P., & Zisserman, A. (2003). Object class recognition by unsupervised scale invariant learning. In CVPR. Los Alamitos: IEEE Computer Society
13. Fergus, R., Perona, P., & Zisserman, A. (2005). A sparse object category model for efficient learning and exhaustive recognition. In CVPR.
14. Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In ICML (pp. 148-156).
15. Friedman, J. H., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view ofboosting. Annals of Statistics, 28, 337-407.
16. Fritz, M., Leibe, B., Caputo, B., & Schiele, B. (2005). Integrating representative and discriminant models for object category detection. In ICCV.
17. Gao, D., & Vasconcelos, N. (2004). Discriminant saliency for visual recognition from cluttered scenes. In NIPS.
18. Holub, A. D., & Perona, P. (2005). A discriminative framework for modeling object classes. In CVPR.
19. Holub, A. D., Welling, M., & Perona, P. (2005). Combining generative models and fisher kernels for object class recognition. In ICCV.
20. Kadir, T., & Brady, M. (2001). Scale, saliency and image description. International Journal of Computer Vision, 45(2), 83-105.
21. Leibe, B., Leonardis, A., & Schiele, B. (2004). Combined object categorization and segmentation with an implicit shape model. In ECCV workshop on statistical learning in computer vision.
22. Li, Y., Shapiro, L., & Bilmes, J. (2005). A generative /discriminative learning algorithm for image classification. In ICCV (Vol. 2, pp. 1605-1612).
23. Loeff, N., Arora, H., Sorokin, A., & Forsyth, D. (2005). Efficient unsupervised learning for localization and detection in object categories. In NIPS.
24. Lowe, D. (2001). Local feature view clustering for 3D object recognition. In CVPR, (pp. 682-688).
25. Mason, L., Baxter, J., Bartlett, P., & Frean, M. (2000). Boosting algorithms as gradient descent in function space. In NIPS (pp. 512-518).
26. Murphy, K. P., Torralba, A., & Freeman, W. T. (2003). Using the forest to see the trees: a graphical model relating features, objects and scenes. In NIPS.
27. NG, A. Y., & Jordan, M. I. (2001). On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In NIPS.
28. Opelt, A., Fussenegger, M., Pinz, A., & Auer, P. (2004a). Object recognition with boosting (Technical report tr-emt-2004-01). Submitted to PAMI.
29. Opelt, A., Fussenegger, M., Pinz, A., & Auer, P. (2004b). Weak hypotheses and boosting for generic object detection and recognition. In ECCV.
30. Schapire, R. E., & Singer, Y. (1999). Improved boosting using confidence-rated predictions. Machine Learning, 37(3), 297-336.
31. Serre, T., Wolf, L., & Poggio, T. (2005). A new biologically motivated framework for robust object recognition. In CVPR.
32. Thureson, J., & Carlsson, S. (2004). Appearance based qualitative image description for object class recognition. In ECCV (pp. 518-529).
33. Torralba, A., Murphy, K., & Freeman, W. T. (2004). Contextual models for object detection using boosted random fields. In NIPS.
34. Ullman, S., Vidal-Naquet, M., & Sali, E. (2002). Visual features of intermediate complexity and their use in classification. Nature Neuroscience, 5, 682-687.
35. Ulusoy, I., & Bishop, C. M. (2005). Generative versus discriminative methods for object recognition. In CVPR (Vol. 2, pp. 258-265).
36. Vapnik, V. N. (1998). Statistical learning theory. New York: Wiley.
37. Vidal-Naquet, M., & Ullman, S. (2003). Object recognition with informative features and linear classification. In ICCV.
38. Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In CVPR.