Performance comparison of ten variations on the interpretation-tree matching algorithm
The best known algorithm for symbolic model matching in computer vision is the Interpretation Tree search algorithm. This algorithm has a high computational complexity when applied to matching problems with large numbers of features. This paper examines ten variations of this algorithm in a search for improved performance, and concludes that the non-wildcard and hierarchical algorithms have reduced theoretical complexity and run faster than the standard algorithm.
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