Abstract
This paper describes a supervised learning approach to classify Automatic Speech Recognition (ASR) transcripts from videos. A training collection was generated using the data provided by the VideoCLEF 2009 framework. These data contained metadata files about videos. The Support Vector Machines (SVM) learning algorithm was used in order to evaluate two main experiments: using the metadata files for generating the training corpus and without using them. The obtained results show the expected increase in precision due to the use of metadata in the classification of the test videos.
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Perea-Ortega, J.M., Montejo-Ráez, A., Martín-Valdivia, M.T., Ureña-López, L.A. (2010). Using Support Vector Machines as Learning Algorithm for Video Categorization. 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_48
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DOI: https://doi.org/10.1007/978-3-642-15751-6_48
Publisher Name: Springer, Berlin, Heidelberg
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