Adaptive Low Resolution Pruning for fast Full Search-equivalent pattern matching
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AbstractSeveral 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.
All Author(s) ListTombari F, Ouyang WL, Di Stefano L, Cham WK
Journal namePattern Recognition Letters
Volume Number32
Issue Number15
Pages2119 - 2127
LanguagesEnglish-United Kingdom
KeywordsFull Search-equivalent; Low Resolution Pruning; Pattern matching; Template matching
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Last updated on 2020-28-07 at 01:42