Name
Abstract | ||
|---|---|---|
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(logn) time. Experimental results on pedestrian detection indicate the efficiency of the proposed method in shape matching. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.patcog.2011.11.025 | Pattern Recognition |
Keywords | Field | DocType |
offline testing operation,offline training,binary search tree structure,node classifier,internal node,weak classifier,binary search tree data,node function,node insertion,binary search tree,online training,probabilistic criterion,hausdorff distance,binary search trees | Tree traversal,Pattern recognition,Treap,Self-balancing binary search tree,Binary tree,Optimal binary search tree,Red–black tree,Artificial intelligence,Machine learning,Mathematics,Interval tree,Search tree | Journal |
Volume | Issue | ISSN |
45 | 6 | 0031-3203 |
Citations | PageRank | References |
5 | 0.43 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolaos Tsapanos | 1 | 26 | 3.87 |
| Anastasios Tefas | 2 | 2055 | 177.05 |
| Nikolaos Nikolaidis | 3 | 108 | 10.31 |
| Ioannis Pitas | 4 | 6478 | 626.09 |