Video Manga: generating semantically meaningful video summaries
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
Optimal Shot Boundary Detection Based on Robust Statistical Models
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
AVE: automated home video editing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Tracking users' capture intention: a novel complementary view for home video content analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Shot-boundary detection: unraveled and resolved?
IEEE Transactions on Circuits and Systems for Video Technology
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Interactive and real-time generation of home video summaries on mobile devices
IMMPD '11 Proceedings of the 2011 international ACM workshop on Interactive multimedia on mobile and portable devices
A novel framework for concept detection on large scale video database and feature pool
Artificial Intelligence Review
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With the rapid explosion of video data, compact representation of videos is becoming more and more desirable for efficient browsing and communication, which leads to a number of research works on video summarization in recent years. Among these works, summaries based on a set of still frames are frequently studied and applied due to its high compactness. However, the representativeness of the selected frames, which are taken as the compact representation of the video or video segment, has not been well studied. It is observed that frame representativeness is highly related to the following elements: image quality, user attention measure, visual details, and displaying duration. It is also observed that users have similar tendency in selecting the most representative frame for a certain video segment. In this paper, we developed a method to examine and evaluate the representativeness of video frames based on learning users' perceptive evaluations.