Proceedings of the 2012 National Conference on Information Technology and Computer Science

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

Authors
Shuguo Yang, Chunxia Li
Corresponding Author
Shuguo Yang
Available Online November 2012.
DOI
https://doi.org/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.
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Proceedings
2012 National Conference on Information Technology and Computer Science
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2012
ISBN
978-94-91216-39-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/citcs.2012.73How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 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  - https://doi.org/10.2991/citcs.2012.73
ID  - Yang2012/11
ER  -