News story segmentation in multiple modalities

In this paper, we describe an approach to segmenting news video based on the perceived shift in content using features spanning multiple modalities. We investigate a number of multimedia features, which serve as potential indicators of a change in story, in order to determine which are the most effective. The efficacy of our approach is demonstrated by the performance of our prototype, where a number of feature combinations demonstrate an up to 18% improvement in WindowDiff score compared to other state of the art story segmenters. In our investigation, there is no, one, clearly superior feature, rather the best segmentation occurs when there is synergy between multiple features. A further investigation into the effect on segmentation performance, while varying the number of training examples versus the number of features used, reveal that having better feature combinations is more important than having more training examples. Our work suggests that it is possible to train robust story segmenters for news video using only a handful of broadcasts, provided a good initial feature selection is made.