Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering

In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the probabilistic boosting-tree automatically constructs a tree in which each node combines a number of weak classifiers (evidence, knowledge) into a strong classifier (a conditional posterior probability). It approaches the target posterior distribution by data augmentation (tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier, which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. Also, clustering is naturally embedded in the learning phase and each sub-tree represents a cluster of certain level. The proposed framework is very general and it has interesting conn...

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Zhuowen Tu
• Keywords: Computer Vision, Conditional Posterior Probability, Conditional Probability, ICCV 2005, Overall Posterior Probability, Target Posterior Distribution, Tree Node