Automatic partitioning of full-motion video
Multimedia Systems
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Clever clustering vs. simple speed-up for summarizing rushes
Proceedings of the international workshop on TRECVID video summarization
A unified approach to shot change detection and camera motion characterization
IEEE Transactions on Circuits and Systems for Video Technology
Object-based video abstraction for video surveillance systems
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
Optimization-based automated home video editing system
IEEE Transactions on Circuits and Systems for Video Technology
Home Video Visual Quality Assessment With Spatiotemporal Factors
IEEE Transactions on Circuits and Systems for Video Technology
Movie2Comics: a feast of multimedia artwork
Proceedings of the international conference on Multimedia
iComics: automatic conversion of movie into comics
Proceedings of the international conference on Multimedia
IMShare: instantly sharing your mobile landmark images by search-based reconstruction
Proceedings of the 20th ACM international conference on Multimedia
Near-lossless semantic video summarization and its applications to video analysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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The daunting yet increasing volume of videos on the Internet brings the challenges of storage and indexing to existing online video services. Current techniques like video compression and summarization are still struggling to achieve the two often conflicting goals of low storage and high visual and semantic fidelity. In this work, we develop a new system for video summarization, called "Near-Lossless Video Summarization" (NLVS), which is able to summarize a video stream with the least information loss by using an extremely small piece of metadata. The summary consists of a set of synthesized mosaics and representative keyframes, a compressed audio stream, as well as the metadata about video structure and motion. Although at a very low compression ratio (i.e., 1/30 of H.264 baseline in average, where traditional compression techniques like H.264 fail to preserve the fidelity), the summary still can be used to reconstruct the original video (with the same duration) nearly without semantic information loss. We show that NLVS is a powerful tool for significantly reducing video storage through both objective and subjective comparisons with state-of-the-art video compression and summarization techniques.