Metric Index: An efficient and scalable solution for precise and approximate similarity search

  • Authors:

  • David Novak;Michal Batko;Pavel Zezula

  • Affiliations:

  • Masaryk University, Brno, Czech Republic;Masaryk University, Brno, Czech Republic;Masaryk University, Brno, Czech Republic

  • Venue:
  • Information Systems
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 data management is still a resisting research challenge. We introduce a novel indexing and searching mechanism called Metric Index (M-Index) that employs practically all known principles of metric space partitioning, pruning, and filtering, thus reaching high search performance while having constant building costs per object. The heart of the M-Index is a general mapping mechanism that enables to actually store the data in established structures such as the B^+-tree or even in a distributed storage. We implemented the M-Index with the B^+-tree and performed experiments on two datasets-the first is an artificial set of vectors and the other is a real-life dataset composed of a combination of five MPEG-7 visual descriptors extracted from a database of up to several million digital images. The experiments put several M-Index variants under test and compare them with established techniques for both precise and approximate similarity search. The trials show that the M-Index outperforms the others in terms of efficiency of search-space pruning, I/O costs, and response times for precise similarity queries. Further, the M-Index demonstrates excellent ability to keep similar data close in the index which makes its approximation algorithm very efficient-maintaining practically constant response times while preserving a very high recall as the dataset grows and even beating approaches designed purely for approximate search.