Adaptive Low Resolution Pruning for fast Full Search-equivalent pattern matching

Authors:
Federico Tombari;Wanli Ouyang;Luigi Di Stefano;Wai-Kuen Cham
Affiliations:
DEIS/ARCES, University of Bologna, Bologna, Italy;Dept. of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China;DEIS/ARCES, University of Bologna, Bologna, Italy;Dept. of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

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.