Exploring the content of a video is typically inefficient due to the linear streamed nature of its media. Video may be seen as a combination of a set of features, the visual track, the audio track and transcription of the spoken words, etc. These features may be viewed as a set of temporally bounded parallel modalities. It is our contention that together these modalities and derived features have the potential to be presented individually or in discrete combination, to allow deeper and more effective content exploration within different parts of a video. This paper presents a novel system for videos' exploration and reports a recent user study conducted to learn usage patterns by offering video content as an alternative representation. The learned usage patterns may be utilized to build a template driven representation engine that uses the features to offer a multimodal synopsis of video that may lead to more efficient exploration of video content.
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