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

CNN-Based Prediction of Frame-Level Shot Importance for Video Summarization