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.

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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
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References

[AFS93]: Rakesh Agrawal, Christos Faloutsos, Arun N. Swami: Efficient Similarity Search In Sequence Databases. FODO 1993: 69-84
[ALSS95]: Rakesh Agrawal, King-Ip Lin, Harpreet S. Sawhney, Kyuseok Shim: Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. VLDB 1995: 490-501
[BBKK97]: Stefan Berchtold, Christian Böhm, Daniel A. Keim, Hans-Peter Kriegel: A Cost Model For Nearest Neighbor Search in High-Dimensional Data Space. PODS 1997: 78-86
[Ber+97]: Stefan Berchtold, Christian Böhm, Bernhard Braunmüller, Daniel A. Keim, Hans-Peter Kriegel: Fast Parallel Similarity Search in Multimedia Databases. SIGMOD Conference 1997: 1-12
[BHKS93]: Thomas Brinkhoff, Holger Horn, Hans-Peter Kriegel, Ralf Schneider: A Storage and Access Architecture for Efficient Query Processing in Spatial Database Systems. SSD 1993: 357-376
[BKK96]: Stefan Berchtold, Daniel A. Keim, Hans-Peter Kriegel: The X-tree : An Index Structure for High-Dimensional Data. VLDB 1996: 28-39
[BKK97]: Stefan Berchtold, Daniel A. Keim, Hans-Peter Kriegel: Using Extended Feature Objects for Partial Similarity Retrieval. VLDB J. 6(4): 333-348(1997)
[BK97]: Stefan Berchtold, Hans-Peter Kriegel: S3: Similarity Search in CAD Database Systems. SIGMOD Conference 1997: 564-567
[BKSS90]: Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, Bernhard Seeger: The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles. SIGMOD Conference 1990: 322-331
[BKSS94]: Thomas Brinkhoff, Hans-Peter Kriegel, Ralf Schneider, Bernhard Seeger: Multi-Step Processing of Spatial Joins. SIGMOD Conference 1994: 197-208
[BR85]: Michael J. Best, Klaus Ritter: Linear Programming: Active Set Analysis and Computer Programs. Prentice-Hall 1985, ISBN 0-13-536996-7
[Fal+94]: Christos Faloutsos, Ron Barber, Myron Flickner, Jim Hafner, Wayne Niblack, Dragutin Petkovic, William Equitz: Efficient and Effective Querying by Image Content. J. Intell. Inf. Syst. 3(3/4): 231-262(1994)
[FRM94]: Christos Faloutsos, M. Ranganathan, Yannis Manolopoulos: Fast Subsequence Matching in Time-Series Databases. SIGMOD Conference 1994: 419-429
[GM93]: James E. Gary, Rajiv Mehrotra: Similar shape retrieval using a structural feature index. Inf. Syst. 18(7): 525-537(1993)
[Gut84]: Antonin Guttman: R-Trees: A Dynamic Index Structure for Spatial Searching. SIGMOD Conference 1984: 47-57
[Haf+95]: James L. Hafner, Harpreet S. Sawhney, William Equitz, Myron Flickner, Wayne Niblack: Efficient Color Histogram Indexing for Quadratic Form Distance Functions. IEEE Trans. Pattern Anal. Mach. Intell. 17(7): 729-736(1995)
[HS95]: Gísli R. Hjaltason, Hanan Samet: Ranking in Spatial Databases. SSD 1995: 83-95
[Jag91]: H. V. Jagadish: A Retrieval Technique for Similar Shapes. SIGMOD Conference 1991: 208-217
[Kor+96]: Flip Korn, Nikolaos Sidiropoulos, Christos Faloutsos, Eliot Siegel, Zenon Protopapas: Fast Nearest Neighbor Search in Medical Image Databases. VLDB 1996: 215-226
[OM88]: Jack A. Orenstein, Frank Manola: PROBE Spatial Data Modeling and Query Processing in an Image Database Application. IEEE Trans. Software Eng. 14(5): 611-629(1988)
[PTVF92]: William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery: Numerical Recipes in C, 2nd Edition. Cambridge University Press 1992
Contents
[RKV95]: Nick Roussopoulos, Stephen Kelley, Frédéic Vincent: Nearest Neighbor Queries. SIGMOD Conference 1995: 71-79
[SRF87]: Timos K. Sellis, Nick Roussopoulos, Christos Faloutsos: The R+-Tree: A Dynamic Index for Multi-Dimensional Objects. VLDB 1987: 507-518