Fast similarity search for high-dimensional dataset

ISM 2006 - 8th IEEE International Symposium on Multimedia

Volumn , Issue , 2006, Pages 799-804

  Quan, Wang ^a   Suya, You ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHALLENGING PROBLEM; EXPERIMENTAL STUDIES; HIGH-DIMENSIONAL; HIGH-DIMENSIONAL DATASET; INTERNATIONAL SYMPOSIUM; MULTI MEDIA DATABASES; NEAREST NEIGHBOR (NN) SEARCH; NEW APPROACHES; PAPER ADDRESSES; SIMILARITY SEARCHES; SPATIAL DATA;

DATABASE SYSTEMS; FEATURE EXTRACTION; FILTRATION; LEAD; PATTERN RECOGNITION;

TECHNICAL PRESENTATIONS;

EID: 46249093152     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISM.2006.78     Document Type: Conference Paper

References (18)

1. R. Bellman. Adaptive Control Processes: A Guided Tour. Princeton University Press, 1961.
2. Roger Weber, Hans-J. Schek, Stephen Blott, A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Space, Proc. 24th Int. Conf. Very Large Data Bases, VLDB, 1998.
3. S. Blott, R. Weber. A Simple Vector-Approximation Fi-le for Similarity Search in High-dimensional Vector paces. Technical Report 19, ESPRIT project HERMES (no.9141), m-arch 1997.
4. J. H. Friedman, J. L. Bentley, and R. A. Finkel. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 3(3):209-226, 1977.
5. Jeffrey S. Beis and David G. Lowe, Shape indexing using approximate nearest-neighbor search in high-dimensional spaces, CVPR, 1997.
6. A. Guttman. R-trees: A dynamic index structure for spatial searching. In Proc. of the ACM SIGMOD Int. Conf. On Management of Data, pages 47-57, Boston, MA, June 1984.
7. N. Beckmann, H.-P. Kriegel, R Schneider, and B. Seeger. The R*-tree: An efficient and robust access method for points and rectangles. In Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data, pages 322-331, Atlantic City, NJ, 23-25 May 1990.
8. N. Katayama and S. Satoh. The SR-tree: An index structure for high-dimensional nearest neighbor queries. In Proc. of the ACM SIGMOD Int.Conj.on Management of Data, pages 369-380, Tucson, Arizon USA, 1997.
9. P. Ciaccia, M. Patella, and P. Zezula. M-tree: An efficient access method for similarity search in metric spaces. In Proc. of the Int. Conference on Very Large Databases, Athens, Greece, 1997.
10. K.-I. Lin, H. Jagadish, and C. Faloutsos. The TV-tree: An index structure for high-dimensional data. The VLDB Journal, 3(4):517-549, Oct. 1994.
11. S. Berchtold, D . Keim, and H.-P. Kriegel. The X-tree: An index structure for high-dimensional data. In Proc. of The Int. Conference on Very Large Databases, pages 28-39, 1996.
12. H. Ferhatosmanoglu, E. Tuncel, D. Agrawal, A. El Abbadi, Vector Approximation based Indexing for Non-uniform High Dimensional Data Sets, CIKM: ACM CIKM 2000.
13. Roger Weber, Klemens Böhm, Trading Quality for Time with Nearest-Neighbor Search, Lecture Notes in Computer Science, 2000.
14. R. Weber, K Böhm, Hans-J. Schek, Interactive-Time Similarity Search for Large Image Collections Using Parallel VA-Files, ICDE, 2000.
15. Stefan Berchtold, Christian Böhm, H. V. Jagadish, Hans-Peter Kriegel, Jörg Sander, Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces, ICDE, 2000.
16. Ertem Tuncel, Hakan Ferhatosmanoglu, Kenneth Rose, VQ-Index: An Index Structure for Similarity Searching in Multimedia Databases, ACMMM, 2002.
17. Paolo Ciaccia, Marco Patella, "PAC Nearest Neighbor Queries: Approximate and Controlled Search in High-Dimensional and Metric Spaces," icde, p. 244, 16th International Conference on Data Engineering (ICDE'00), 2000.
18. David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, IJCV, 2004.