Learning to Identify TV News Monologues by Style and Context

摘要

This research focused on the problem of learning semantics from multimedia data associated with broadcast video documents. The authors proposed to learn semantic concepts from multimodal sources based on style and context detectors, in combination with statistical classifier ensembles. As a case study, they present their method for detecting the concept of news subject monologues. This approach had the best average precision performance amongst 26 submissions in the 2003 video track of the Text Retrieval Conference benchmark. Experiments were conducted with respect to individual detector contribution, ensemble size, and ranking mechanism. It was found that the combination of detectors is decisive for the final result, although some detectors might appear useless in isolation. Moreover, by using a probabilistic ranking in combination with a large classifier ensemble, the results can be improved even further.