Shape matching using a binary search tree structure of weak classifiers

In this paper, a novel algorithm for shape matching based on the Hausdorff distance and a binary search tree data structure is proposed. The shapes are stored in a binary search tree that can be traversed according to a Hausdorff-like similarity measure that allows us to make routing decisions at any given internal node. Each node functions as a classifier that can be trained using supervised learning. These node classifiers are very similar to perceptrons, and can be trained by formulating a probabilistic criterion for the expected performance of the classifier, then maximizing that criterion. Methods for node insertion and deletion are also available, so that a tree can be dynamically updated. While offline training is time consuming, all online training and both online and offline testing operations can be performed in O ( log n ) time. Experimental results on pedestrian detection indicate the efficiency of the proposed method in shape matching.