Object category recognition using boosting tree with heterogeneous features

The problem of object category recognition has long challenged the computer vision community. In this paper, we address these tasks via learning two-class and multi-class discriminative models. The proposed approach integrates the Adaboost algorithm into the decision tree structure, called DB-Tree, and each tree node combines a number of weak classifiers into a strong classifier (a conditional posterior probability). In the learning stage, each boosted classifier in a tree node is trained to split the training set to left and right sub-trees, and the classifier is thus used not to return the class of the sample but rather to assign the sample to the left or right sub-tree. Therefore, the DB-Tree can be built up automatically and recursively. In the testing stage, the posterior probability of each node is computed by the weighted conditional probability of left and right sub-trees. Thus, the top node of the tree can output the overall posterior probability. In addition, the multi-class and two-class learning procedures become unified, through treating the multi-class classification problem as a special two-class classification problem, and either a positive or negative label is assigned to each class in minimizing the total entropy in each node.