  With the explosion of video content on the Internet, there is a need for
research on methods for video analysis which take human cognition into account.
One such cognitive measure is memorability, or the ability to recall visual
content after watching it. Prior research has looked into image memorability
and shown that it is intrinsic to visual content, but the problem of modeling
video memorability has not been addressed sufficiently. In this work, we
develop a prediction model for video memorability, including complexities of
video content in it. Detailed feature analysis reveals that the proposed method
correlates well with existing findings on memorability. We also describe a
novel experiment of predicting video sub-shot memorability and show that our
approach improves over current memorability methods in this task. Experiments
on standard datasets demonstrate that the proposed metric can achieve results
on par or better than the state-of-the art methods for video summarization.
