Efficient User-Adaptable Similarity Search in Large Multimedia Databases.
Thomas Seidl, Hans-Peter Kriegel:
Efficient User-Adaptable Similarity Search in Large Multimedia Databases.
VLDB 1997: 506-515@inproceedings{DBLP:conf/vldb/SeidlK97,
author = {Thomas Seidl and
Hans-Peter Kriegel},
editor = {Matthias Jarke and
Michael J. Carey and
Klaus R. Dittrich and
Frederick H. Lochovsky and
Pericles Loucopoulos and
Manfred A. Jeusfeld},
title = {Efficient User-Adaptable Similarity Search in Large Multimedia
Databases},
booktitle = {VLDB'97, Proceedings of 23rd International Conference on Very
Large Data Bases, August 25-29, 1997, Athens, Greece},
publisher = {Morgan Kaufmann},
year = {1997},
isbn = {1-55860-470-7},
pages = {506-515},
ee = {db/conf/vldb/SeidlK97.html},
crossref = {DBLP:conf/vldb/97},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
Abstract
Efficient user-adaptable similarity search more and more increases in
its importance for multimedia and spatial database systems. As a
general similarity model for multi-dimensional vectors that is
adaptable to application requirements and user preferences, we use
quadratic form distance functions dA^2(x,y)=(x-y)*A*(x-y)^T which have
been successfully applied to color histograms in image databases
[Faloutsos et al. 1994]. The components aij of the matrix A denote
similarity of the components i and j of the vectors. Beyond the
Euclidean distance which produces spherical query ranges, the
similarity distance defines a new query type, the ellipsoid query. We
present new algorithms to efficiently support ellipsoid query
processing for various user-defined similarity matrices on existing
precomputed indexes. By adapting techniques for reducing the
dimensionality and employing a multi-step query processing
architecture, the method is extended to high-dimensional data spaces.
In particular, from our algorithm to reduce the similarity matrix, we
obtain the greatest lower-bounding similarity function thus
guaranteeing no false drops. We implemented our algorithms in C++ and
tested them on an image database containing 12,000 color
histograms. The experiments demonstrate the flexibility of our
method in conjunction with a high selectivity and efficiency.
Copyright © 1997 by the VLDB Endowment.
Permission to copy without fee all or part of this material is granted provided that the copies are not made or
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is by the permission of the Very Large Data Base
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Online Paper
CDROM Version: Load the CDROM "Volume 1 Issue 5, VLDB '89-'97" and ...
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Printed Edition
Matthias Jarke, Michael J. Carey, Klaus R. Dittrich, Frederick H. Lochovsky, Pericles Loucopoulos, Manfred A. Jeusfeld (Eds.):
VLDB'97, Proceedings of 23rd International Conference on Very Large Data Bases, August 25-29, 1997, Athens, Greece.
Morgan Kaufmann 1997, ISBN 1-55860-470-7
Contents
Electronic Edition
From CS Dept.,
University Trier (Germany)
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Copyright © Tue Mar 16 02:22:07 2010
by Michael Ley (ley@uni-trier.de)