In the Internet, ubiquitous presence of redundant, unedited, raw videos has
made video summarization an important problem. Traditional methods of video
summarization employ a heuristic set of hand-crafted features, which in many
cases fail to capture subtle abstraction of a scene. This paper presents a deep
learning method that maps the context of a video to the importance of a scene
similar to that is perceived by humans. In particular, a convolutional neural
network (CNN)-based architecture is proposed to mimic the frame-level shot
importance for user-oriented video summarization. The weights and biases of the
CNN are trained extensively through off-line processing, so that it can provide
the importance of a frame of an unseen video almost instantaneously.
Experiments on estimating the shot importance is carried out using the publicly
available database TVSum50. It is shown that the performance of the proposed
network is substantially better than that of commonly referred feature-based
methods for estimating the shot importance in terms of mean absolute error,
absolute error variance, and relative F-measure.

本文提出了一种基于卷积神经网络的用户定向视频摘要生成方法，与传统的基于手工特征的方法相比，通过离线训练得到的权重和偏差使得该系统可以几乎实时地提取视频帧的重要性，实验结果表明该方法在估计视频镜头重要性方面表现优异。