2 research outputs found
Improving retrieval accuracy of Hierarchical Cellular Trees for generic metric spaces
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.Peer Reviewe