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Performance Prediction for Unsupervised Video Indexing

  • Ralph Ewerth
  • Bernd Freisleben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5702)

Abstract

Recently, performance prediction has been successfully applied in the field of information retrieval for content analysis and retrieval tasks. This paper discusses how performance prediction can be realized for unsupervised learning approaches in the context of video content analysis and indexing. Performance prediction helps in identifying the number of detection errors and can thus support post-processing. This is demonstrated for the example of temporal video segmentation by presenting an approach for automatically predicting the precision and recall of a video cut detection result. It is shown for the unsupervised cut detection approach that the related clustering validity measure is highly correlated with the precision of a detection result. Three regression methods are investigated to exploit the observed correlation. Experimental results demonstrate the feasibility of the proposed performance prediction approach.

Keywords

Performance prediction video indexing video retrieval 

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References

  1. 1.
    Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Predicting Query Performance. In: Proceedings of the 25th Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR 2002), Tampere, Finland, pp. 299–306 (2002)Google Scholar
  2. 2.
    Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Precision Prediction based on Ranked List Coherence. Information Retrieval 9(6), 723–755 (2006)CrossRefGoogle Scholar
  3. 3.
    Ester, M., Sander, J.: Knowledge Discovery in Databases. Springer, Berlin (2000)zbMATHGoogle Scholar
  4. 4.
    Ewerth, R., Freisleben, B.: Video Cut Detection Without Thresholds. In: Proceedings of the 11th International Workshop on Signals, Systems and Image Processing, Poznan, Poland, pp. 227–230 (2004)Google Scholar
  5. 5.
    He, B., Ounis, I.: Inferring Query Performance Using Pre-retrieval Predictors. In: Apostolico, A., Melucci, M. (eds.) SPIRE 2004. LNCS, vol. 3246, pp. 43–54. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    MPEG-7: ISO/IEC 15938: Information Technology - Multimedia Content Description Interface Part 2: Description Definition Language. International Organization for Standardization (2002)Google Scholar
  7. 7.
    Smola, A., Schölkopf, B.: A Tutorial on Support Vector Regression. Statistics and Computing 14(3), 199–222 (2004)CrossRefMathSciNetGoogle Scholar
  8. 8.
    TRECVID: TREC Video Retrieval Evaluation, http://www-nlpir.nist.gov/projects/t01v (March 27, 2009)
  9. 9.
    Witten, I.H., Frank, E.: Data Mining - Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, Elsevier Inc. (2005)Google Scholar
  10. 10.
    Yeo, B.L., Liu, B.: Rapid Scene Analysis on Compressed Video. IEEE Transactions on Circuits and Systems for Video Technology 5(6), 533–544 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ralph Ewerth
    • 1
  • Bernd Freisleben
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of MarburgMarburgGermany

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