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 connections to a number of existing methods such as the A* algorithm, decision tree algorithms, generative models, and cascade approaches. In this paper, we show the applications of PBT for classification, detection, and object recognition. We have also applied the framework in segmentation

1. Introduction

The task of classifying/recognizing, detecting, and clustering general objects in natural scenes is extremely challenging. The difficulty is due to many reasons: large intraclass variation and inter-class similarity, articulation and motion, different lighting conditions, orientations/viewing directions, and the complex configurations of different objects. The first row of Fig. (1) displays some face images. The second row shows some typical images from the Caltech-101 categories of objects [5]. Some of them are highly non-rigid and some of the objects in the same category bear little similarity among each other. For the categorization task, it requires very high level knowledge to put different instances of a class into the same category.