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.

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881M (15 November 2007); doi: 10.1117/12.749921

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)