Object category recognition using boosting tree with heterogenous features
Liang Lin, Caiming Xiong, Yue Liu, Yongtian Wang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
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.