Abstract

We describe a baseline system for the VideoCLEF Vid2RSS task in which videos are to be classified into thematic categories based on their content. The system uses an off-the-shelf Information Retrieval system. Speech transcripts generated using automated speech recognition are indexed using default stemming and stopping methods. The categories are populated by using the category theme (or label) as a query on the collection, and assigning the retrieved items to that particular category. Run 4 of our system achieved the highest f-score in the task by maximising recall. We discuss this in terms of the primary aims of the task, i.e., automating video classification.

Keywords

Classification Information Retrieval Automatic Speech Recognition 

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References

  1. 1.
    Apache Software Foundation. Lucene: Java-based Indexing and Searching technology, http://lucene.apache.org/
  2. 2.
    Larson, M., Newman, E., Jones, G.J.F.: Overview of VideoCLEF2008: Automatic Generation of Topic-based Feeds for Dual Language Audio-Visual Content. In: Peters, C., et al. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 906–917. Springer, Heidelberg (2009)Google Scholar
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    Porter, M.: An algorithm for suffix stripping. Program (July 1980)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Eamonn Newman
    • 1
  • Gareth J. F. Jones
    • 1
  1. 1.Centre for Digital Video ProcessingDublin City UniversityDublin 9Ireland

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