Abstract
2009 marked UAIC’s first participation at the VideoCLEF evaluation campaign. Our group built two separate systems for the “Subject Classification” and “Affect Detection” tasks. For the first task we created two resources starting from Wikipedia pages and pages identified with Google and used two tools for classification: Lucene and Weka. For the second task we extracted the audio component from a given video file, using FFmpeg. After that, we computed the average amplitude for each word from the transcript, by applying the Fast Fourier Transform algorithm in order to analyze the sound. A brief description of our systems’ components is given in this paper.
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References
Hatcher, E., Gospodnetic, O.: Lucene in action. Manning Publications Co. (2005)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005) Retrieved 2007-06-25
Larson, M., Newman, E., Jones, G.J.F.: Overview of VideoCLEF 2009: New Perspectives on Speech-based Multimedia Content Enrichment. In: Peters, C., Gonzalo, J., Jones, G.J.F., Muller, H., Tsikrika, T., Kalpathy-Kramer, J. (eds.) CLEF 2009 Workshop, Part II. LNCS, vol. 6242, pp. 354–368. Springer, Heidelberg (2010)
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Dobrilă, TA., Diaconaşu, MC., Lungu, ID., Iftene, A. (2010). Methods for Classifying Videos by Subject and Detecting Narrative Peak Points. In: Peters, C., et al. Multilingual Information Access Evaluation II. Multimedia Experiments. CLEF 2009. Lecture Notes in Computer Science, vol 6242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15751-6_47
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DOI: https://doi.org/10.1007/978-3-642-15751-6_47
Publisher Name: Springer, Berlin, Heidelberg
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