Using support vector machines as learning algorithm for video categorization

Authors:
José Manuel Perea-Ortega;Arturo Montejo-Ráez;María Teresa Martín-Valdivia;L. Alfonso Ureña-López
Affiliations:
SINAI Research Group, Computer Science Department, University of Jaén, Jaén, Spain;SINAI Research Group, Computer Science Department, University of Jaén, Jaén, Spain;SINAI Research Group, Computer Science Department, University of Jaén, Jaén, Spain;SINAI Research Group, Computer Science Department, University of Jaén, Jaén, Spain
Venue:
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Year:
2009

Citing 5
Cited 1

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Overview of VideoCLEF 2009: new perspectives on speech-based multimedia content enrichment

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Overview of VideoCLEF 2009: new perspectives on speech-based multimedia content enrichment

CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments

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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 Video-CLEF 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.