Probabilistic Skimlets Fusion for Summarizing Multiple Consumer Landmark Videos

Abstract

It is difficult to develop a computational model that can accurately predict the quality of the video summary. This paper proposes a novel algorithm to summarize one-shot landmark videos. The algorithm can optimally combine multiple unedited consumer video skims into an aesthetically pleasing summary. In particular, to effectively select the representative key frames from multiple videos, an active learning algorithm is derived by taking advantage of the locality of the frames within each video. Toward a smooth video summary, we define skimlet, a video clip with adjustable length, starting frame, and positioned by each skim. Thereby, a probabilistic framework is developed to transfer the visual cues from a collection of aesthetically pleasing photos into the video summary. The length and the starting frame of each skimlet are calculated to maximally smoothen the video summary. At the same time, the unstable frames are removed from each skimlet. Experiments on multiple videos taken from different sceneries demonstrated the aesthetics, the smoothness, and the stability of the generated summary.