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Using web sources for improving video categorization

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Abstract

In this paper, several experiments about video categorization using a supervised learning approach are presented. To this end, the VideoCLEF 2008 evaluation forum has been chosen as experimental framework. After an analysis of the VideoCLEF corpus, it was found that video transcriptions are not the best source of information in order to identify the thematic of video streams. Therefore, two web-based corpora have been generated in the aim of adding more informational sources by integrating documents from Wikipedia articles and Google searches. A number of supervised categorization experiments using the test data of VideoCLEF have been accomplished. Several machine learning algorithms have been proved to validate the effect of the corpus on the final results: Naïve Bayes, K-nearest-neighbors (KNN), Support Vectors Machine (SVM) and the j48 decision tree. The results obtained show that web can be a useful source of information for generating classification models for video data.

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Notes

  1. http://www.wikipedia.org/

  2. http://www.google.com/

  3. http://www.sigwac.org.uk/

  4. http://www.clef-campaign.org/

  5. http://www.cdvp.dcu.ie/VideoCLEF/

  6. SMART Project. Stop word List for English Information Retrieval, available in http://www.unine.ch/info/clef/englishST.txt.

  7. Snowball stemmer is available in http://snowball.tartarus.org/.

  8. RapidMiner is available from http://rapid-i.com/.

  9. Weka is a set of data mining algorithms and tools easily integrated in RapidMiner. More information is available at http://www.cs.waikato.ac.nz/ml/weka/.

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Acknowledgements

This paper has been partially supported by a grant from the Spanish Government, project TEXT-COOL 2.0 (TIN2009-13391-C04-02), project GEOASIS (P08-TIC-41999) granted by the Andalusian Government and project RFC/PP2008/UJA-08-16-14. We would like to thank the Cross-Language Evaluation Forum in general and Carol Peters in particular.

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Authors and Affiliations

  1. SINAI Research Group, Computer Science Department, University of Jaén, 23008, Jaén, Spain

    José M. Perea-Ortega, Arturo Montejo-Ráez, M. Teresa Martín-Valdivia & L. Alfonso Ureña-López

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  1. José M. Perea-Ortega
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  2. Arturo Montejo-Ráez
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  3. M. Teresa Martín-Valdivia
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  4. L. Alfonso Ureña-López
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Correspondence to José M. Perea-Ortega.

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Perea-Ortega, J.M., Montejo-Ráez, A., Martín-Valdivia, M.T. et al. Using web sources for improving video categorization. J Intell Inf Syst 36, 117–130 (2011). https://doi.org/10.1007/s10844-010-0123-6

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  • DOI: https://doi.org/10.1007/s10844-010-0123-6

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