Automatic Indexing of News Videos Through Text Classification Techniques

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.