On indexing metric spaces using cut-regions

After two decades of research, the techniques for efficient similarity search in metric spaces have combined virtually all the available tricks resulting in many structural index designs. As the representative state-of-the-art metric access methods (also called metric indexes) that vary in the usage of filtering rules and in structural designs, we could mention the M-tree, the M-Index and the List of Clusters, to name a few. In this paper, we present the concept of cut-regions that could heavily improve the performance of metric indexes that were originally designed to employ simple ball-regions. We show that the shape of cut-regions is far more compact than that of ball-regions, yet preserving simple and concise representation. We present three re-designed metric indexes originating from the above-mentioned ones but utilizing cut-regions instead of ball-regions. We show that cut-regions can be fully utilized in the index structure, positively affecting not only query processing but also the index construction. In the experiments we show that the re-designed metric indexes significantly outperform their original versions.