A model of motion attention for video skimming

One of the key issues in video manipulation is video abstraction in the form of skimmed video. For this purpose, an important task is to determine the content significance of each chunk of frames in a video sequence. In this paper, we present a new computational model of motion attention and the approach to applying this model in video skimming. The effectiveness of our architecture and model is demonstrated by user studies of visual skimming experiments. The results indicate that the precision of motion attention detection is over 80%, and the user satisfaction of visual skimming is beyond 70%.