Semi-automatic Categorization of Videos on VideoLectures.net

Authors:
Miha Grcar;Dunja Mladenic;Peter Kese
Affiliations:
Dept. of Knowledge Discovery, Jozef Stefan Institute, Ljubljana, Slovenia 1000;Dept. of Knowledge Discovery, Jozef Stefan Institute, Ljubljana, Slovenia 1000;Viidea Ltd., Kranj, Slovenia 4000
Venue:
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Year:
2009

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Abstract

Automatic or semi-automatic categorization of items (e.g. documents) into a taxonomy is an important and challenging machine-learning task. In this paper, we present a module for semi-automatic categorization of video-recorded lectures. Properly categorized lectures provide the user with a better browsing experience which makes her more efficient in accessing the desired content. Our categorizer combines information found in texts associated with lectures and information extracted from various links between lectures in a unified machine-learning framework. By taking not only texts but also the links into account, the classification accuracy is increased by 12---20%.