Discriminative modeling by boosting on multilevel aggregates

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Corso, Jason J ^a  

^a UNIVERSITY AT BUFFALO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTER VISION; FEATURE EXTRACTION; IMAGE PROCESSING; LABELING; PATTERN RECOGNITION;

ADAPTIVE FEATURES; DISCRIMINATIVE MODELS; NATURAL IMAGES; NEW APPROACHES; NEW CLASS; PERFORMANCE MEASURES; SHAPE PROPERTIES; TWO-CLASS PROBLEMS;

AGGREGATES;

EID: 51949109617     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587489     Document Type: Conference Paper

References (19)

1. S. Avidan. Spatialboost: Adding spatial reasoning to adaboost. In Proc. of ECCV, pages 386-396, 2006.
2. E. Borenstein, E. Sharon, and S. Ullman. Combining Top-down and Bottom-Up Segmentation. In Proc. of CVPR, 2004.
3. E. Borenstein and S. Ullman. Class-Specific, Top-Down Segmentation. In Proc. of ECCV, 2002.
4. Y. Freund and R. E. Schapire. A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting. J. Comp. and Sys. Sci., 55(1):119-139, 1997.
5. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting. Tech. Rpt., Statistics, Stanford Univ., 1998.
6. E. Grossmann. Adatree: Boosting a weak classifier into a decision tree. In Proc. of CVPR Workshop v. 6, 2004.
7. C. E. Guo, S. C. Zhu, and Y. N. Wu. Primal sketch: Integrating texture and structure. J. of Comp. Vis. and Img. Und., 2006.
8. M. P. Kumar, P. H. S. Torr, and A. Zisserman. OBJ CUT. In Proc. of CVPR, pp. 18-25, 2005.
9. J. Lafferty, A. McCallum, and F. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proc. of ICML, 2001.
10. X. Ren, C. C. Fowlkes, and J. Malik. Cue Integration for Figure/Ground Labeling. In Proc. of NIPS, 2005.
11. E. Sharon, A. Brandt, and R. Basri. Fast Multiscale Image Segmentation. In Proc. of CVPR, v. I, pp. 70-77, 2000.
12. J. Shotton, J. Winn, C. Rother, and A. Criminisi. TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation. In Proc. of ECCV, 2006.
13. A. Torralba, K. Murphy, and W. T. Freeman. Sharing Features: Efficient Boosting Procedures for Multiclass Object Detection. In Proc. of CVPR, 2004.
14. Z. Tu. Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering. In Proc. of ICCV, 2005.
15. Z. Tu and S. C. Zhu. Image Segmentation by Data-Driven Markov Chain Monte Carlo. IEEE Trans. on PAMI, 24(5):657-673, 2002.
16. P. Viola and M. J. Jones. Robust Real-Time Face Detection. Intl. J. of Comp. Vis., 57(2):137-154, 2004.
17. T. F. Wu, G. S. Xia, and S. C. Zhu. Compositional boosting for computing hierarchical image structures. In Proc. of CVPR, pp. 1-8, 2007.
18. Z. Yao, X. Yang, and S. C. Zhu. Introduction to a Large Scale General Purpose Ground Truth Dataset: Methodology, Annotation Tool, and Benchmarks. In Proc. of EMMCVPR, 2007.
19. S.-F. Zheng, Z. Tu, and A. Yuille. Detecting Object Boundaries Using Low-, Mid-, and High-Level Information. In Proc. of CVPR, 2007.