A semi-automatic approach to home video editing
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Exploring Video Structure Beyond The Shots
ICMCS '98 Proceedings of the IEEE International Conference on Multimedia Computing and Systems
To learn representativeness of video frames
Proceedings of the 13th annual ACM international conference on Multimedia
An interactive and multi-level framework for summarising user generated videos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
IEEE Transactions on Circuits and Systems for Video Technology
Introducing risplayer: real-time interactive generation of personalized video summaries
Proceedings of the 2010 ACM workshop on Social, adaptive and personalized multimedia interaction and access
Modeling and Mining of Users' Capture Intention for Home Videos
IEEE Transactions on Multimedia
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
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With the proliferation of mobile devices and multimedia, videos have become an indispensable part of life-logs for personal experiences. In this paper, we present a real-time and interactive application for home video summarization on mobile devices. The main challenge of this method is lack of information about the video content in the following frames, which we term "partial-context" in this paper. First of all, real-time segmentation algorithm based on partial-context is applied to decompose the captured video into segments in line with the change in dominant camera motion. Secondly, the main challenge to conventional video summarization is the semantic understanding of the video content. Thus, we leverage the fact that it is easy to get user input on a mobile device and attack this problem through the user interaction. The user preference is learned and modeled by a Gaussian Mixture Model (GMM), which is updated each time when users manually select key frames. Evaluation results demonstrate that our system significantly improves user experience and provides an efficient automatic/semi-automatic video summarization solution for mobile users.