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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.