Only overlay text: novel features for TV news broadcast video segmentation

Article Type

Research Article

Publication Title

Multimedia Tools and Applications


Segmentation of television news videos into programs and stories (after removing advertisements) is a necessary first step for news broadcast analysis. Existing methods have used manually defined presentation styles as an important feature for such segmentation. Manually defined presentation styles make algorithms channel specific and hampers scalability for large number of channels. In this work, we advocate the usebility of overlay text for automatic characterization of broadcast presentation styles. This automatic characterization will minimize the manual intervention required in developing the scalable solutions for television news broadcast segmentation. To this end, we introduce three novel features solely derived from position and content of overlay text bands. These are Bag of Bands (BoB), BoB Templates (BoBT) and Text-based Semantic Similarity (TSS). The BoB features characterize on-screen distribution of text bands and are used with classifiers for advertisement detection. The BoBT features characterize co-occurrence of text bands. Thereby modeling the presentation styles of video shots. Sequences of BoBT features are modeled using Conditional Random Fields (CRFs) for identifying program boundaries. Sequences of features derived from semantic similarity (TSS) between consecutive shots and BoBT feature are used with CRFs for story segmentation. Performances of the proposed features are validated on 360 hours of broadcast data recorded from three Indian English news channels. Benchmark on baseline methods has shown better performance of our proposal.

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