Improving retrieval accuracy of Hierarchical Cellular Trees for generic metric spaces

Abstract

Abstract Metric Access Methods (MAMs) are indexing techniques which al- low working in generic metric spaces. Therefore, MAMs are specially useful for Content-Based Image Retrieval systems based on features which use non Lp norms as similarity measures. MAMs naturally allow the design of image browsers due to their inherent hierarchical structure. The Hierarchical Cellular Tree (HCT), a MAM-based indexing technique, provides the starting point of our work. In this paper, we describe some limitations detected in the original formulation of the HCT and propose some modi cations to both the index building and the search algorithm. First, the covering radius, which is de ned as the distance from the representative to the furthest element in a node, may not cover all the elements belonging to the node's subtree. Therefore, we pro- pose to rede ne the covering radius as the distance from the representative to the furthest element in the node's subtree. This new de nition is essen- tial to guarantee a correct construction of the HCT. Second, the proposed Progressive Query retrieval scheme can be redesigned to perform the nearest neighbor operation in a more e cient way. We propose a new retrieval scheme which takes advantage of the bene ts of the search algorithm used in the index building. Furthermore, while the evaluation of the HCT in the original work was only subjective, we propose an objective evaluation based on two aspects which are crucial in any approximate search algorithm: the retrieval time and the retrieval accuracy. Finally, we illustrate the usefulness of the proposal by presenting some actual applications.