Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
Text filtering by boosting naive Bayes classifiers
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A Multi-Modal Approach to Story Segmentation for News Video
World Wide Web
Combining experts for anchorperson shot detection in news videos
Pattern Analysis & Applications
Combining audio-based and video-based shot classification systems for news videos segmentation
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper we discuss about the applicability of text classification techniques for automatic content recognition of the scenes from news videos. In particular, the news scenes are classified according to a predefined set of six categories (National Politics, National News, World, Finance, Society & Culture and Sports) by applying text classification techniques on the transcription of the anchorman speech. The transcription is obtained using a commercial tool for speech to text. The application of text classification techniques for the automatic indexing of news videos is not new in the scientific literature, but, to the best of our knowledge, no paper reports a detailed experimentation. In our experimentations we considered different issues concerning the application of text categorization and speech recognition for news story classification: in fact, we calculated the overall performance obtained by using text categorization on the ideal transcription, as it could be obtained by employing a perfect speech recognition engine, and the transcription provided by a commercial speech recognition tool; furthermore, in our experimentation we were also interested to characterize the performance in terms of the portion of the news story by which the transcription is obtained. The experimentations have been carried out on a database of Italian news videos. This experimental validation represents the main contribution of this paper.