An Indexing Approach for Representing Multimedia Objects in High-Dimensional Spaces Based on Expectation Maximization Algorithm

1. Baeza-Yates, R., Cunto, W., Manber, U., Wu, S.: Proximity matching using Fixed-queries trees. In: Crochemore, M., Gusfield, D. (eds.) CPM 1994. LNCS, vol. 807, pp. 198–212. Springer, Heidelberg (1994)

   Google Scholar 

2. Banerjee, A., Dhillon, I.S., Ghosh, J., Sra, S.: Clustering on hyperspheres using Expectation Maximization. Technical Report TR-03-07, Department of Computer Sciences, University of Texas (February 2003)

   Google Scholar 

3. Bilmes, J.A.: A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Technical Report, U.C. Berkeley (April 1998)

   Google Scholar 

4. Boccignone, G., Chianese, A., Moscato, V., Picariello, A.: Fovaeted Shot Detection for Video Segmentation. IEEE Trans. on Circuits and Sistems for Video Technology 15(3) (March 2005)

   Google Scholar 

5. Brin, S.: Near neighbor search in large metric spaces. In: Proc. of VLDB 1995, Switzerland, pp. 574–584 (1995)

   Google Scholar 

6. Burkhard, W.A., Keller, R.M.: Some approaches to best-match file searching. Comm. of the ACM 16(4), 230–236 (1973)

   Article  MATH  Google Scholar 

7. Celeux, G., Govaert, G.: A classification EM algorithm for clustering and two stochastic versions. Computational Statistics and Data Analysis 14, 315–332 (1992)

   Article  MATH  MathSciNet  Google Scholar 

8. Chavez, E., Navarro, G., Baeza-Yates, R., Marroquin, J.L.: Searching in Metric Spaces. ACM Computing Surveys (1999)

   Google Scholar 

9. Ciaccia, P., Patella, M., Zezula, P.: M-tree: An efficient Access Method for Similarity Search in Metric Spaces. In: Proc. of 23rd International Conference on VLDB, pp. 426–435 (1997) FQ tree 0..5825 FQ tree (with bag distance) 0.4124

   Google Scholar 

10. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data. Journal of the Royal Statistical Society 39, 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

11. Ganti, V., Ramakrishnan, R., Gehrke, J., Powell, A., French, J.: Clustering large datasets in arbitrary metric spaces. In: The Proceedings of International Conference on Data Engineering (1999)

    Google Scholar 

12. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1998)

    Google Scholar 

13. Kalantari, I., McDonald, G.: A data structure and an algorithm for the nearest point problem. IEEE Transactions on Software Engineering 9(5) (1983)

    Google Scholar 

14. Neal, R.M., Hinton, G.E.: A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan, M.J. (ed.) Learning in Graphical Models, pp. 355–368. MIT Press, Cambridge (1998)

    Google Scholar 

15. Uhlmann, J.: Satisying general proximity/similarity queries with metric trees. Information Processing Letters 40, 175–179 (1991)

    Article  MATH  Google Scholar 

16. Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms, January 25-27, pp. 311–321 (1993)

    Google Scholar 

17. Yu, D., Zhang, A.: ClusterTree: Integration of Cluster Representation and Nearest-Neighbor Search for Large Data Sets with High Dimensions. IEEE Trans. on KDE 15(5), 1316–1330 (2003)

    MathSciNet  Google Scholar 

18. Zhong, S., Ghosh, J.: A Unified Framework for Model-based Clustering. Journal of Machine Learning Research 4, 1001–1037 (2003)

    Article  MathSciNet  Google Scholar 

19. Wu, J.-K.: Content-Based Indexing of Multimedia Databases. IEEE Transactions on Knowledge and Data Engineering 9(6) (1997)

    Google Scholar 

20. Bohm, C., Berchtold, S., Keim, D.A.: Searching in High-Dimensional Spaces-Index Structures for Improving the Performance of Multimedia Databases. ACM Computing Surveys 33(3) (2001)

    Google Scholar 

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