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
Year2011
Month11
Day1
Volume Number32
Issue Number15
PublisherElsevier
Pages2119 - 2127
ISSN0167-8655
eISSN1872-7344
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