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Comparison of Feature Construction Methods for Video Relevance Prediction

  • Pablo Bermejo
  • Hideo Joho
  • Joemon M. Jose
  • Robert Villa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5371)

Abstract

Low level features of multimedia content often have limited power to discriminate a document’s relevance to a query. This motivated researchers to investigate other types of features. In this paper, we investigated four groups of features: low-level object features, behavioural features, vocabulary features, and window-based vocabulary features, to predict the relevance of shots in video retrieval. Search logs from two user studies formed the basis of our evaluation. The experimental results show that the window-based vocabulary features performed best. The behavioural features also showed a promising result, which is useful when the vocabulary features are not available. We also discuss the performance of classifiers.

Keywords

video retrieval relevance prediction feature construction 

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References

  1. 1.
    Agichtein, E., Brill, E., Dumais, S.: Improving web search ranking by incorporating user behavior information. In: SIGIR 2006: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 19–26. ACM Press, New York (2006)Google Scholar
  2. 2.
    Bekkerman, R., McCallum, A., Huang, G.: Automatic categorization of email into folders: Bechmark experiments on enron and sri corpora. Technical report, Department of Computer Science. University of Massachusetts, Amherst (2005)Google Scholar
  3. 3.
    Bermejo, P., Gámez, J., Puerta, J.: On incremental wrapper-based attribute selection: experimental analysis of the relevance criteria. In: IPMU 2008: Proceedings of the 12th Intl. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2008)Google Scholar
  4. 4.
    Flores, M.J., Gámez, J., Mateo, J.L.: Mining the esrom: A study of breeding value classification in manchego sheep by means of attribute selection and construction. Computers and Electronics in Agriculture 60(2), 167–177 (2007)CrossRefGoogle Scholar
  5. 5.
    Freitas, A.A.: Understanding the crucial role of attributeinteraction in data mining. Artif. Intell. Rev. 16, 177–199 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Geng, X., Liu, T.-Y., Qin, T., Li, H.: Feature selection for ranking. In: SIGIR 2007: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 407–414. ACM, New York (2007)Google Scholar
  7. 7.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  8. 8.
    Howarth, P., Rüger, S.M.: Evaluation of texture features for content-based image retrieval. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 326–334. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Hu, Y.-J.: Constructive induction: covering attribute spectrum In Feature Extraction, Construction and Selection: a data mining perspective. Kluwer, Dordrecht (1998)Google Scholar
  10. 10.
    Liu, H., Motoda, H.: Feature Extraction Construction and Selection: a data mining perspective. Kluwer Academic Publishers, Dordrecht (1998)CrossRefzbMATHGoogle Scholar
  11. 11.
    McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: AAAI/ICML 1998 Workshop on Learning for Text Categorization, pp. 41–48 (1998)Google Scholar
  12. 12.
    Otero, F., Silva, M., Freitas, A., NIevola, J.: Genetic programming for attribute construction in data mining. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Porter, M.F.: An algorithm for suffix stripping, pp. 313–316 (1997)Google Scholar
  14. 14.
    Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M., Gatford, M.: Okapi at TREC-3. In: Proceedings of the Third Text REtrieval Conference (TREC 1994), Gaithersburg, USA (1994)Google Scholar
  15. 15.
    Ruiz, R., Riquelme, J.C., Aguilar-Ruiz, J.S.: Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recogn. 39, 2383–2392 (2006)CrossRefGoogle Scholar
  16. 16.
    Sikora, T.: The mpeg-7 visual standard for content description-an overview.  11(6), 696–702 (June 2001)Google Scholar
  17. 17.
    Smeaton, A.F., Over, P., Kraaij, W.: Evaluation campaigns and trecvid. In: MIR 2006: Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval, pp. 321–330. ACM Press, New York (2006)Google Scholar
  18. 18.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  19. 19.
    Sparck Jones, K.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28(1), 11–21 (1972)CrossRefGoogle Scholar
  20. 20.
    Villa, R., Gildea, N., Jose, J.M.: Facetbrowser: a user interface for complex search tasks. In: ACM Multimedia 2008 (in press, 2008)Google Scholar
  21. 21.
    Villa, R., Gildea, N., Jose, J.M.: Joint conference on digital libraries. In: A Study of Awareness in Multimedia Search, pp. 221–230 (June 2008)Google Scholar
  22. 22.
    Yan, R., Hauptmann, A.G.: Co-retrieval: A boosted reranking approach for video retrieval. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 60–69. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Pablo Bermejo
    • 1
  • Hideo Joho
    • 2
  • Joemon M. Jose
    • 2
  • Robert Villa
    • 2
  1. 1.Computing Systems Dept.Universidad de Castilla-La ManchaAlbaceteSpain
  2. 2.Department of Computing ScienceUniversity of GlasgowUK

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