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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Newman, E., Jones, G.J.F. (2009). DCU at VideoClef 2008. In: Peters, C., et al. Evaluating Systems for Multilingual and Multimodal Information Access. CLEF 2008. Lecture Notes in Computer Science, vol 5706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04447-2_121
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DOI: https://doi.org/10.1007/978-3-642-04447-2_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04446-5
Online ISBN: 978-3-642-04447-2
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