Chapter

Evaluating Systems for Multilingual and Multimodal Information Access

Volume 5706 of the series Lecture Notes in Computer Science pp 923-926

DCU at VideoClef 2008

  • Eamonn NewmanAffiliated withCarnegie Mellon UniversityCentre for Digital Video Processing, Dublin City University
  • , Gareth J. F. JonesAffiliated withCarnegie Mellon UniversityCentre for Digital Video Processing, Dublin City University

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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