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Video Event Classification Using Bag of Words and String Kernels

  • Lamberto Ballan
  • Marco Bertini
  • Alberto Del Bimbo
  • Giuseppe Serra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

The recognition of events in videos is a relevant and challenging task of automatic semantic video analysis. At present one of the most successful frameworks, used for object recognition tasks, is the bag-of-words (BoW) approach. However this approach does not model the temporal information of the video stream. In this paper we present a method to introduce temporal information within the BoW approach. Events are modeled as a sequence composed of histograms of visual features, computed from each frame using the traditional BoW model. The sequences are treated as strings where each histogram is considered as a character. Event classification of these sequences of variable size, depending on the length of the video clip, are performed using SVM classifiers with a string kernel that uses the Needlemann-Wunsch edit distance. Experimental results, performed on two datasets, soccer video and TRECVID 2005, demonstrate the validity of the proposed approach.

Keywords

video annotation action classification bag-of-words string kernel edit distance 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lamberto Ballan
    • 1
  • Marco Bertini
    • 1
  • Alberto Del Bimbo
    • 1
  • Giuseppe Serra
    • 1
  1. 1.Media Integration and Communication CenterUniversity of FlorenceItaly

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