A Comparison of Unsupervised Shot Classification Algorithms for News Video Segmentation

  • Massimo De Santo
  • Gennaro Percannella
  • Carlo Sansone
  • Mario Vento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)


Automatic classification of shots extracted by news video plays an important role in the context of news video segmentation. In spite of the efforts of the researchers involved in this field, a definite solution for the shot classification problem does not yet exist. Moreover, the authors of each novel algorithm usually provide results supporting the claim that their method performs well on a set of news videos, without facing the problem of making a wide comparison with other algorithms in terms of key performance indexes.

In this paper, we present an experimental comparison of three shot classification algorithms. We considered only techniques that do not require the explicit definition of a model of the specific news video. In such a way the obtained performance should be quite independent of the news program’s style. For testing the selected algorithms, we built up a database significantly wider than those typically used in the field.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Massimo De Santo
    • 1
  • Gennaro Percannella
    • 1
  • Carlo Sansone
    • 2
  • Mario Vento
    • 1
  1. 1.Dipartimento di Ingegneria dell’Informazione e di Ingegneria ElettricaUniversità di SalernoFiscianoItaly
  2. 2.Dipartimento di Informatica e SistemisticaUniversità di Napoli ”Federico II”NapoliItaly

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