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Adaptive Low Resolution Pruning for fast Full Search-equivalent pattern matching
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DOI：10.1016/j.patrec.2011.07.030.png)
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Abstract
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Several recent proposals have shown the feasibility of significantly speeding-up pattern matching by means of Full Search-equivalent techniques, i.e. without approximating the outcome of the search with respect to a brute force investigation. These techniques are generally heavily based on efficient incremental calculation schemes aimed at avoiding unnecessary computations. In a very recent and extensive experimental evaluation, Low Resolution Pruning turned out to be in most cases the best performing approach. In this paper we propose a computational analysis of several incremental techniques specifically designed to enhance the efficiency of LRP. In addition, we propose a novel LRP algorithm aimed at minimizing the theoretical number of operations by adaptively exploiting different incremental approaches. We demonstrate the effectiveness of our proposal by means of experimental evaluation on a large dataset. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Pattern matching
Template matching
Low Resolution Pruning
Full Search-equivalent
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