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

Federico Tombari, Wanli Ouyang, Luigi di Stefano, Wai-kuen Cham

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. Highlights ► A computational analysis of incremental techniques is carried out. ► This analysis is specifically designed to enhance the efficiency of LRP algorithm. ► A novel LRP algorithm is proposed, minimizing the theoretical number of operations. ► The novel LRP algorithm adaptively exploits different incremental approaches. ► The effectiveness of the proposed algorithm is demonstrated on a vast image dataset.