  Video skimming, also known as dynamic video summarization, generates a
temporally abridged version of a given video. Skimming can be achieved by
identifying significant components either in uni-modal or multi-modal features
extracted from the video. Being dynamic in nature, video skimming, through
temporal connectivity, allows better understanding of the video from its
summary. Having this obvious advantage, recently, video skimming has drawn the
focus of many researchers benefiting from the easy availability of the required
computing resources. In this paper, we provide a comprehensive survey on video
skimming focusing on the substantial amount of literature from the past decade.
We present a taxonomy of video skimming approaches, and discuss their evolution
highlighting key advances. We also provide a study on the components required
for the evaluation of a video skimming performance.

Computer Vision and Pattern Recognition;Image and Video Processing