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Interactive Content-Based Retrieval Using Pre-computed Object-Object Similarities

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Image and Video Retrieval (CIVR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3115))

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Abstract

We propose using truncated object-object similarity matrix as an access structure for interactive video retrieval. The proposed approach offers a scalable solution to retrieval and allows combination of different feature spaces or sources of information. Experiments were performed on TREC Video collections of 2002 and 2003.

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References

  1. TRECVID 2003 Workshop, Notebook Papers (2003)

    Google Scholar 

  2. Aksoy, S., Haralick, R.M.: Feature normalization and likelihood-based similarity measures for image retrieval. Pattern Recognition Letters 22(5), 563–582 (2001)

    Article  MATH  Google Scholar 

  3. Cox, J., Miller, M.L., Minka, T.P., Papathomas, T.V., Yianilos, P.N.: The bayesian image retrieval system, PicHunter: Theory, implementation, and psychophysical experiments. IEEE Tran. On Image Processing 9(1), 20–37 (2000)

    Article  Google Scholar 

  4. Drucker, H., Shahrary, B., Gibbon, D.C.: Relevance feedback using support vector machines. In: Proc. 18th Int. Conf. on Machine Learning, pp. 122–129. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  5. Voorhees, E.M. (ed.): Proc. 10th Text Retrieval Conference, TREC-10 (2002)

    Google Scholar 

  6. Faloutsos, C.: Searching Multimedia Databases By Content. Kluwer Academic Publishers, Boston (1996)

    MATH  Google Scholar 

  7. Flickner, M., Sawhney, H., Niblack, W., Ashley, J.: Query by image and video content: the QBIC system. IEEE Computer 28, 310–315 (1995)

    Google Scholar 

  8. Gauvain, J.L., Lamel, L., Adda, G.: The LIMSI broadcast news transcription system. Speech Communication 37(1-2), 89–108 (2002)

    Article  MATH  Google Scholar 

  9. Hiemstra, D.: Using language models for information retrieval. PhD thesis, Centre for Telematics and Information Technology, University of Twente (2001)

    Google Scholar 

  10. Ishikawa, Y., Subramanya, R., Faloutsos, C.: MindReader: Querying databases through multiple examples. In: Proc. 24th Int. Conf. Very Large Data Bases, pp. 218–227 (1998)

    Google Scholar 

  11. Laaksonen, J., Koskela, M., Oja, E.: PicSOM: Self-organizing maps for contentbased image retrieval. In: Proc. of IJCNN 1999, Washington, D.C., USA (1999)

    Google Scholar 

  12. Robertson, S.E.: The probability ranking principle in IR. Journal of Documentation 33(4), 294–304 (1977)

    Article  Google Scholar 

  13. Rocchio, J.J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The Smart Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. Prentice Hall, Englewood Cliffs (1971)

    Google Scholar 

  14. Rui, Y., Huang, T.: Optimizing learning in image retrieval. In: Proc. IEEE int. Conf. On Computer Vision and Pattern Recognition (June 2000)

    Google Scholar 

  15. Tong, S., Chang, E.: Support vector machine active learning for image retrieval. In: Proc. 9th ACM Int. Conf. on Multimedia, pp. 107–118. ACM Press, New York (2001)

    Chapter  Google Scholar 

  16. Vasconcelos, N.M.: Bayesian Models for Visual Information Retrieval. PhD thesis, Massachusetts Institute of Technology (2000)

    Google Scholar 

  17. Voorhees, E.M.: Variations in relevance judgments and the measurement of retrieval effectiveness. Information Processing Management 36(5), 697–716 (2000)

    Article  Google Scholar 

  18. Weisstein, E.W.: CRC Concise Encyclopedia of Mathematics. CRC Press, Boca Raton (2002)

    Book  Google Scholar 

  19. Westerveld, T., de Vries, A.P., van Ballegooij, A.R., de Jong, F.M.G., Hiemstra, D.: A probabilistic multimedia retrieval model and its evaluation. EURASIP Journal on Applied Signal Processing 2, 186–198 (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Databases Group, University of Twente, P.O.Box 217, 7500 AE, Enschede, The Netherlands

    Liudmila Boldareva & Djoerd Hiemstra

Authors
  1. Liudmila Boldareva
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  2. Djoerd Hiemstra
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Editor information

Editors and Affiliations

  1. School of Computing, Mathematical and Information Sciences, University of Brighton, UK

    Peter Enser

  2. Informatics and Telematics Institute, Centre for Research and Technology-Hellas, 57001, Thessaloniki, Greece

    Yiannis Kompatsiaris

  3. Centre for Digital Video Processing, Adaptive Information Cluster, Dublin City University, Ireland

    Noel E. O’Connor

  4. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  5. ISLA lab, Informatics Institute, University of Amsterdam, The Netherlands

    Arnold W. M. Smeulders

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© 2004 Springer-Verlag Berlin Heidelberg

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Boldareva, L., Hiemstra, D. (2004). Interactive Content-Based Retrieval Using Pre-computed Object-Object Similarities. In: Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds) Image and Video Retrieval. CIVR 2004. Lecture Notes in Computer Science, vol 3115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27814-6_38

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  • DOI: https://doi.org/10.1007/978-3-540-27814-6_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22539-3

  • Online ISBN: 978-3-540-27814-6

  • eBook Packages: Springer Book Archive

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