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Volumn , Issue , 2006, Pages 317-326

Efficient top-k hyperplane query processing for multimedia information retrieval

Author keywords

Kernel based methods; Retrieval; Support vector machines

Indexed keywords

HYPERPLANE-BASED QUERY; KERNEL BASED METHODS; RANGE-QUERY PROBLEMS;

EID: 34547223405     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1180639.1180712     Document Type: Conference Paper
Times cited : (15)

References (28)
  • 1
    • 0028257199 scopus 로고
    • An optimal algorithm for approximate nearest neighbor searching in fixed dimensions
    • S. Arya, D. Mount, N. Netanyahu, R. Silverman, and A. Wu. An optimal algorithm for approximate nearest neighbor searching in fixed dimensions. In Proceedings of the 5th SODA, pages 573-82, 1994.
    • (1994) Proceedings of the 5th SODA , pp. 573-582
    • Arya, S.1    Mount, D.2    Netanyahu, N.3    Silverman, R.4    Wu, A.5
  • 5
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167, 1998.
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.2 , pp. 121-167
    • Burges, C.J.C.1
  • 6
    • 10044235999 scopus 로고    scopus 로고
    • LIBSVM: A library for support vector machines
    • available at
    • C-C. Chang and C-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/ libsvm.
    • (2001) Software
    • Chang, C.-C.1    Lin, C.-J.2
  • 7
    • 34547197103 scopus 로고    scopus 로고
    • Putting active learning into multimedia applications; dynamic definition and refinement of concept classifiers
    • M.-Y. Chen, M. Christel, A. Hauptmann, and H. Wactlar. Putting active learning into multimedia applications; dynamic definition and refinement of concept classifiers. In ACM Multimedia, 2005.
    • (2005) ACM Multimedia
    • Chen, M.-Y.1    Christel, M.2    Hauptmann, A.3    Wactlar, H.4
  • 8
    • 0033906160 scopus 로고    scopus 로고
    • P. Ciaccia and M. Patella. Pac nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces. In In Proceedings of International Conference on Data Engineering, pages 244-255, 2000.
    • P. Ciaccia and M. Patella. Pac nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces. In In Proceedings of International Conference on Data Engineering, pages 244-255, 2000.
  • 9
    • 84993661659 scopus 로고    scopus 로고
    • M-tree: An efficient access method for similarity search in metric spaces
    • P. Ciaccia, M. Patella, and P. Zezula. M-tree: An efficient access method for similarity search in metric spaces. VLDB, pages 426-435, 1997.
    • (1997) VLDB , pp. 426-435
    • Ciaccia, P.1    Patella, M.2    Zezula, P.3
  • 10
    • 0000913324 scopus 로고    scopus 로고
    • SVMTorch: Support vector machines for large-scale regression problems
    • R. Collobert and S. Bengio. SVMTorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research, 1:143-160, 2001.
    • (2001) Journal of Machine Learning Research , vol.1 , pp. 143-160
    • Collobert, R.1    Bengio, S.2
  • 12
    • 15044355327 scopus 로고    scopus 로고
    • Similarity search in high dimensions via hashing
    • A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In The VLDB Journal, pages 518-529, 1999.
    • (1999) The VLDB Journal , pp. 518-529
    • Gionis, A.1    Indyk, P.2    Motwani, R.3
  • 13
    • 0021615874 scopus 로고
    • R-trees: A dynamic index structure for spatial searching
    • B. Yormark, editor, Boston, Massachusetts, June 18-21, ACM Press
    • A. Guttman. R-trees: A dynamic index structure for spatial searching. In B. Yormark, editor, SIGMOD'84, Proceedings of Annual Meeting, Boston, Massachusetts, June 18-21, 1984, pages 47-57. ACM Press, 1984.
    • (1984) SIGMOD'84, Proceedings of Annual Meeting , pp. 47-57
    • Guttman, A.1
  • 14
    • 0037700825 scopus 로고    scopus 로고
    • Learning and inferring a semantic space from user's relevance feedback for image retrieval
    • X. He, W.-Y. Ma, O. King, M. Li, and H. Zhang. Learning and inferring a semantic space from user's relevance feedback for image retrieval. In ACM Multimedia, pages 343-346, 2002.
    • (2002) ACM Multimedia , pp. 343-346
    • He, X.1    Ma, W.-Y.2    King, O.3    Li, M.4    Zhang, H.5
  • 15
    • 34547224349 scopus 로고    scopus 로고
    • Fast approximate similarity search in extremely high-dimensional data sets
    • M. E. Houle and J. Sakuma. Fast approximate similarity search in extremely high-dimensional data sets. In ICDE, 2004.
    • (2004) ICDE
    • Houle, M.E.1    Sakuma, J.2
  • 16
    • 0031644241 scopus 로고    scopus 로고
    • Approximate nearest neighbors: Towards removing the curse of dimensionality
    • P. Indyk and R. Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. In Proc. of 30th STOC, pages 604-613, 1998.
    • (1998) Proc. of 30th STOC , pp. 604-613
    • Indyk, P.1    Motwani, R.2
  • 17
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale svm learning practical
    • Schölkopf, Burges, and Smola, editors, MIT-Press
    • T. Joachims. Making large-scale svm learning practical. In Schölkopf, Burges, and Smola, editors, Advances in Kernel Methods - Support Vector Learning. MIT-Press, 1999.
    • (1999) Advances in Kernel Methods - Support Vector Learning
    • Joachims, T.1
  • 18
    • 0031162081 scopus 로고    scopus 로고
    • The SR-tree: An index structure for high-dimensional nearest neighbor queries
    • N. Katayama and S. Satoh. The SR-tree: an index structure for high-dimensional nearest neighbor queries. In ACM SIGMOD Int. Conf. on Management of Data, pages 369-380, 1997.
    • (1997) ACM SIGMOD Int. Conf. on Management of Data , pp. 369-380
    • Katayama, N.1    Satoh, S.2
  • 19
    • 34547231883 scopus 로고    scopus 로고
    • Tutorial on high-dimensional index structures: Database support for next decades applications
    • D. A. Keim. Tutorial on high-dimensional index structures: Database support for next decades applications. In Proceedings of the ICDE, 2000.
    • (2000) Proceedings of the ICDE
    • Keim, D.A.1
  • 21
    • 34249762939 scopus 로고
    • The tv-tree: An index structure for high-dimensional data
    • K.-I. Lin, H. V. Jagadish, and C. Faloutsos. The tv-tree: An index structure for high-dimensional data. VLDB Journal, 3(4):517-542, 1994.
    • (1994) VLDB Journal , vol.3 , Issue.4 , pp. 517-542
    • Lin, K.-I.1    Jagadish, H.V.2    Faloutsos, C.3
  • 22
    • 84883098972 scopus 로고    scopus 로고
    • Learning the semantics of multimedia queries and concepts from a small number of examples
    • A. P. Natsev, M. R. Naphade, and J. Tešić. Learning the semantics of multimedia queries and concepts from a small number of examples. In ACM Multimedia, pages 598-607, 2005.
    • (2005) ACM Multimedia , pp. 598-607
    • Natsev, A.P.1    Naphade, M.R.2    Tešić, J.3
  • 24
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • B. Schölkopf, C Burges, and A. Smola, editors
    • J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C Burges, and A. Smola, editors, Advances in Kernel Methods -Support Vector Learning, 1998.
    • (1998) Advances in Kernel Methods -Support Vector Learning
    • Platt, J.1
  • 25
    • 0021644214 scopus 로고
    • The quadtree and related hierarchical data structures
    • H. Samet. The quadtree and related hierarchical data structures. ACM Computing Surveys, 16(2), pages 187-260, 1984.
    • (1984) ACM Computing Surveys , vol.16 , Issue.2 , pp. 187-260
    • Samet, H.1
  • 27
    • 0003007938 scopus 로고    scopus 로고
    • Support vector machine active learning with applications to text classification
    • P. Langley, editor, Stanford, US, Morgan Kaufmann Publishers, San Francisco, US
    • S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In P. Langley, editor, Proceedings of ICML, pages 999-1006, Stanford, US, 2000. Morgan Kaufmann Publishers, San Francisco, US.
    • (2000) Proceedings of ICML , pp. 999-1006
    • Tong, S.1    Koller, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.