Probabilistic Cascade Random Fields for Man-Made Structure Detection

摘要

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.