Abstract
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.
Keywords
- Interaction Potential
- Association Potential
- Terminal Node
- Markov Random Fields
- Conditional Random Field
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Besag, J.: On the Statistical Analysis of Dirty Pictures. J. of Royal Statisticial Society B-48, 259–302 (1986)
Freund, Y., Schapire, R.E.: A Decision-theoretic Generalization of on-line Learning and an Application to Boosting. J. of Computer and Sys. Sci. 55 (1997)
Friedman, J., Hastie, T., Tibshirani, R.: Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics 28 (2000)
Lafferty, J., McCallum, A., Pereira, F.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proc. of ICML (2001)
Lee, C., Greiner, R., Schmidt, M.: Support Vector Random Fields for Spatial Classification. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 121–132. Springer, Heidelberg (2005)
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Tokyo (2001)
Kolmogorov, V., Zabih, R.: What Energy Functions can be Minimized via Graph Cuts? IEEE Trans. on PAMI 26(2), 147–159 (2004)
Kumar, S., Hebert, M.: Man-Made Structure Detection in Natural Images using a Causal Multiscale Random Field. In: Proc. of CVPR (2003)
Kumar, S., Hebert, M.: Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification. In: Proc. of ICCV (2003)
Kumar, S., Hebert, M.: Discriminative Fields for Modeling Spatial Dependencies in Natural Images. In: Proc. of NIPS (2004)
Platt, J.C.: Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In: Advances in Large Margin Classifiers, pp. 61–74 (1999)
Porikli, F.: Integral Histogram: a Fast Way to Extract Histograms in Cartesian Spaces. In: Proc. of CVPR (2005)
Šochman, J., Matas, J.: WaldBoost – Learning for Time Constrained Sequential Detection. In: Proc. of CVPR (2005)
Tu, Z.: Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering. In: Proc. of ICCV (October 2005)
Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Proc. of CVPR (December 2001)
Wu, J., Brubaker, S.C., Mullin, M.D., Rehg, J.M.: Fast Asymmetric Learning for Cascade Face Detection. IEEE Trans. on PAMI 30(3), 369–382 (2008)
Xiao, R., Zhu, L., Zhang, H.: Boosting Chain Learning for Object Detection. In: Proc. of ICCV (October 2003)
Zhang, J., Zhou, S.K., McMillan, L., Comaniciu, D.: Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network. In: Proc. of CVPR (2007)
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Zheng, S. (2010). Probabilistic Cascade Random Fields for Man-Made Structure Detection. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_56
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DOI: https://doi.org/10.1007/978-3-642-12304-7_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12303-0
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