Experimental analysis of insertion costs in a naïve dynamic MDF-tree

Abstract

Similarity search is a widely employed technique in Pattern Recognition. In order to speed up the search many indexing techniques have been proposed. However, the majority of the proposed techniques are static, that is, a fixed training set is used to build up the index. This characteristic becomes a major problem when these techniques are used in dynamic (interactive) systems. In these systems updating the training set is necessary to improve its performance. In this work, we explore the surprising efficiency of a naïve algorithm that allows making incremental insertion in a previously known index: the MDF-tree.