VideoCut: Removing Irrelevant Frames by Discovering the Object of Interest

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We propose a novel method for removing irrelevant frames from a video given user-provided frame-level labeling for a very small number of frames. We first hypothesize a number of candidate areas which possibly contain the object of interest, and then figure out which area(s) truly contain the object of interest. Our method enjoys several favorable properties. First, compared to approaches where a single descriptor is used to describe a whole frame, each area’s feature descriptor has the chance of genuinely describing the object of interest, hence it is less affected by background clutter. Second, by considering the temporal continuity of a video instead of treating the frames as independent, we can hypothesize the location of the candidate areas more accurately. Third, by infusing prior knowledge into the topic-motion model, we can precisely follow the trajectory of the object of interest. This allows us to largely reduce the number of candidate areas and hence reduce the chance of overfitting the data during learning. We demonstrate the effectiveness of the method by comparing it to several other semi-supervised learning approaches on challenging video clips.