Metric space is a universal and versatile model of similarity that can be applied in various
areas of information retrieval. However, a general, efficient, and scalable solution for metric …

The main paradigm of similarity searching in metric spaces has remained mostly unchanged
for decades—data objects are organized into a hierarchical structure according to their …

The second volume of this popular handbook demonstrates the richness and breadth of the
IS and IT disciplines. The book explores their close links to the practice of using, managing …

Most researchers working on high-dimensional indexing agree on the following three
trends:(i) the size of the multimedia collections to index are now reaching millions if not …

Metric indexes are traditionally used for organizing unstructured or complex data to speed
up similarity queries. The most widely-used indexes cluster data or divide space using hyper …

This work is a companion reproducible paper of a previous paper (Antol et al., 2021) in
which we presented an alternative to the traditional paradigm of similarity searching in …

This paper presents the NV-Tree (Nearest Vector Tree). It addresses the specific, yet
important, problem of efficiently and effectively finding the approximate k-nearest neighbors …

This article addresses the problem of matching the most similar data objects to a given query
object. We adopt a generic model of similarity that involves the domain of objects and metric …

In all subfields of information retrieval, test datasets and ground truth data are important tools
for testing and comparison of new search methods. This is also reflected by the image …

Multimedia content sharing within social networks has become one of the most interesting
and trending research fields over the last few years. This undoubted emerge of related …