A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Markov random field modeling in image analysis
Markov random field modeling in image analysis
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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Fast Asymmetric Learning for Cascade Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Man-made structure detection in natural images using a causal multiscale random field
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Support vector random fields for spatial classification
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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This paper develops the probabilistic version of cascade algorithm, specifically, Probabilistic AdaBoost Cascade (PABC). The proposed PABC algorithm is further employed to learn the association potential in the Discriminative Random Fields (DRF) model, resulting the Probabilistic Cascade Random Fields (PCRF) model. PCRF model enjoys the advantage of incorporating far more informative features than the conventional DRF model. Moreover, compared to the original DRF model, PCRF is less sensitive to the class imbalance problem. The proposed PABC and PCRF were applied to the task of man-made structure detection. We compared the performance of PABC with different settings, the performance of the original DRF model and that of PCRF. Detailed numerical analysis demonstrated that PABC improves the performance with more AdaBoost nodes, and the interaction potential in PCRF further improves the performance significantly.