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Using web sources for improving video categorization
Abstract In this paper, several experiments about vIn 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: Naive 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.ting classification models for video data.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Comments web can be a useful source of information for generating classification models for video data
Conclusion Our main conclusion is that it is possibleOur main conclusion is that it is possible to use the information from web sources in order to improve the results for the video categorization system. Firstly, the experiments with Google and/or Wikipedia overcome the baseline using the VideoCLEF corpus in all cases. On the other hand, the corpus with Google works better than the Wikipedia one. However, it is very interesting that the combined use of Google and Wikipedia obtains the best results for all the machine learning algorithms. Based on these results, we conclude that the informal content found on the web, probably helps to enrich the learning corpora used to train video categorization systems.sed to train video categorization systems.
Data source Wikipedia pages  +
Doi 10.1007/s10844-010-0123-6 +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Using%2Bweb%2Bsources%2Bfor%2Bimproving%2Bvideo%2Bcategorization%22  +
Has author José M. Perea-Ortega + , Arturo Montejo-Ráez + , M. Teresa Martín-Valdivia + , L. Alfonso Ureña-López +
Has domain Computer science +
Has topic Multimedia information retrieval +
Peer reviewed Yes  +
Publication type Journal article  +
Published in Journal of Intelligent Information Systems +
Research design Statistical analysis  +
Research questions In this paper, several experiments about video categorization using a supervised learning approach are presented.
Revid 11,026  +
Theories Undetermined
Theory type Design and action  +
Title Using web sources for improving video categorization
Unit of analysis Article  +
Url http://dx.doi.org/10.1007/s10844-010-0123-6  +
Wikipedia coverage Other  +
Wikipedia data extraction Dump  +
Wikipedia language English  +
Wikipedia page type Article  +
Year 2010  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:32:24  +
Categories Multimedia information retrieval  + , Computer science  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:32:13  +
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