DynDex: A dynamic and non-metric space indexer

Proceedings of the ACM International Multimedia Conference and Exhibition

Volumn , Issue , 2002, Pages 466-475

  Goh, King Shy ^a   Li, Beitao ^a   Chang, Edward ^a  

^a University of California   (United States)

Author keywords

High dimensional index; Non metric distance function; Similarity search

Indexed keywords

COGNITIVE SYSTEMS; DATA MINING; DATABASE SYSTEMS; MULTIMEDIA SYSTEMS;

HIGH-DIMENSIONAL INDEX;

CONTENT BASED RETRIEVAL;

EID: 0038715065     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. C. C. Aggarwal, A. Hinneburg, and D. A. Keim. On the surprising behavior of distance metrics in high dimensional spaces. Proceedings of ICDT, 2001.
2. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. Proceedings of ACM SIGMOD, June 1998.
3. N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The R*-tree: an efficient and robust access method for points and rectangles. Proceedings of ACM SIGMOD, May 1990.
4. S. Berchtold, D. A. Keim, and H.-P. Kriegel. The X-Tree: An index structure for high-dimensional data. Proceedings of the 22nd VLDB, August 1996.
5. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is "nearest neighbor" meaningful? Proceedings of ICDT, 1999.
6. T. Bozkaya and M. Ozsoyoglu. Distanced-based indexing for high-dimensional metric spaces. Proc. of ACM SIGMOD, pages 357-368, 1997.
7. P. S. Bradley, U. M. Fayyad, and C. A. Reina. Scaling em (expectation-maximization) clustering to large databases. Microsoft Technical Report MSR-TR-98-34, November 1999.
8. L. Breiman. Bagging predicators. Machine Learning, pages 123-140, 1996.
9. P. Ciaccia, M. Patella, and P. Zezula. M-Tree: An efficient access method for similarity search in metric spaces. Proceedings of the 23rd VLDB, August 1997.
10. M. Ester, H.-P. Kriegel, and J. Sander. Focussing technique for efficient class identification. Proc. of the 4th Intl. Sym. of Large Spatial Databases, 1995.
11. M. Flickner, H. Sawhney, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. Query by image and video content: the QBiC system. IEEE Computer, 28(9):23-32, 1995.
12. A. Gersho and R. Gray. Vector Quantization and Signal Compression. Kluwer Academic, 1991.
13. K.-S. Goh, B. Li, and E. Chang. Indexing non-metric perceptual distance functions through statistical classification (extended version). www-db.stanford.edu/̃echang/dyndex-extended.ps, 2002.
14. A. Hinneburg, C. C. Aggarwal, and D. A. Keim. What is the nearest neighbor in high dimensional spaces? Proceedings of the 26th VLDB, September 2000.
15. P. Indyk, A. Gionis, and R. Motwani. Similarity search in high dimensions via hashing. Proceedings of the 25th VLDB, September 1999.
16. Y. Ishikawa, R. Subramanya, and C. Faloutsos. Mindreader: Querying databases through multiple examples. VLDB, 1998.
17. D. W. Jacobs, D. Weinshall, and Y. Gdalyahu. Classification with non-metric distances: Image retrieval and class representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2000.
18. N. Katayama and S. Satoh. The SR-Tree: An index structure for high-dimensional nearest neighbor queries. Proceedings of ACM SIGMOD, May 1997.
19. B. Li, E. Chang, and Y. Wu. Discovery of a perceptual distance function for measuring similarity. ACM Multimedia Journal Special Issue (to appear), 2002.
20. C. Li, E. Chang, H. Garcia-Molina, and G. Wiederhold. Clustering for approximate similarity queries in high-dimensional spaces. IEEE Transaction on Knowledge and Data Engineering (to appear), 2001.
21. K.-L. Lin, H. V. Jagadish, and C. Faloutsos. The TV-tree: an index structure for high-dimensional data. VLDB Journal, 3(4), 1994.
22. D. L. Medin, R. L. Goldstone, and D. Gentner. Respects for similarity. Psychological Review, 100(2):254-278, 1993.
23. G. Murphy and D. Medin. The role of theories in conceptual coherence. Psychological Review, 92:289-316, 1985.
24. R. T. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. Proceedings of the 20th VLDB, September 1994.
25. K. Porkaew, K. Chakrabarti, and S. Mehrotra. Query refinement for multimedia similarity retrieval in mars. Proceedings of ACM Multimedia, November 1999.
26. K. Porkaew, S. Mehrota, and M. Ortega. Query reformulation for content based multimedia retrieval in mars. ICMcS, pages 747-751, 1999.
27. J. Rocchio. Relevance feedback in information retrieval. In G. Salton. editor, The SMART retrival system: Experiments in automatic document processing. Prentice-Hall, 1971.
28. Y. Rui, T. S. Huang, and S. Mehrotra. Content-based image retrieval with relevance feedback in mars. Proc. IEEE Int. Conf. on Image Proc., 1997.
29. S. Tong and E. Chang. Support vector machine active learning for image retrieval. ACM International Conference on Multimedia, October 2001.
30. A. Tversky. Feature of similarity. Psychological Review, 84:327-352, 1977.
31. J. Z. Wang, J. Li, and G. Wiederhold. Wise: A wavelet-based image search engine with efficient feature vector clustering and classification. Submitted for journal publication, 1998.
32. Y. Wu, E. Chang, and B. Li. Video shot detection. UCSB Technical Report (submitted to ICME 2002), 2002.
33. T. Zhang, R. Ramakrishnan, and M. Livny. Birch: An efficient data clustering method for very large databases. Proceedings of ACM SIGMOD, June 1996.
34. X. S. Zhou and T. S. Huang. Comparing discriminating transformations and svm for learning during multimedia retrieval. Proc. of ACM Conf. on Multimedia, pages 137-146, 2001.