Boosting MBR based kNN Search over Multimedia Data by Approximate Pruning Metric

DOI

10.2991/citcs.2012.73How to use a DOI?

Keywords

MBR; kNN search; Multimedia indexing; Multidimensional pruning

Abstract

MBR (Minimum Bounding Rectangle) has been widely used to represent multimedia data objects in R*-Tree family indexing techniques. In this paper, in order to improve the performance of kNN searching over multimedia data, we propose an approach to reduce the computation cost of MINMAXDIST by using its approximate upper bound instead of its precise value, and then we use it to construct two stronger heuristics for kNN pruning, which are helpful to avoid visiting unnecessary data objects and MBRs. The experimental results show that the proposed approach can reduce the computation cost and boost the overall performance in R*-Tree based kNN searching tasks.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Shuguo Yang
AU  - Chunxia Li
PY  - 2012/11
DA  - 2012/11
TI  - Boosting MBR based kNN Search over Multimedia Data by Approximate Pruning Metric
BT  - Proceedings of the 2012 National Conference on Information Technology and Computer Science
PB  - Atlantis Press
SP  - 276
EP  - 279
SN  - 1951-6851
UR  - https://doi.org/10.2991/citcs.2012.73
DO  - 10.2991/citcs.2012.73
ID  - Yang2012/11
ER  -