Boundary Error Analysis and Categorization in the TRECVID News Story Segmentation Task

  • Joaquim Arlandis
  • Paul Over
  • Wessel Kraaij
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3568)


In this paper, an error analysis based on boundary error popularity (frequency) including semantic boundary categorization is applied in the context of the news story segmentation task from TRECVID. Clusters of systems were defined based on the input resources they used including video, audio and automatic speech recognition. A cross-popularity specific index was used to measure boundary error popularity across clusters, which allowed goal-driven selection of boundaries to be categorized. A wide set of boundaries was viewed and a summary of the error types is presented. This framework allowed conclusions about the behavior of resource-based clusters in the context of news story segmentation.


False Alarm Boundary Error Automatic Speech Recognition Test Collection Input Resource 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Joaquim Arlandis
    • 1
  • Paul Over
    • 2
  • Wessel Kraaij
    • 3
  1. 1.Departament d’Informàtica de Sistemes i ComputadorsUniversitat Politècnica de ValènciaValènciaSpain
  2. 2.Retrieval Group, Information Access DivisionNational Institute of Standards and TechnologyGaithersburgUSA
  3. 3.Department of Data InterpretationInformation Systems Division, TNO Science & IndustryDelftThe Netherlands

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