Communications of the ACM
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)
Nonchronological Video Synopsis and Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time-Constrained Keyframe Selection Technique
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
A Statistical Framework for Image Category Search from a Mental Picture
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2010 papers
Video tapestries with continuous temporal zoom
ACM SIGGRAPH 2010 papers
Motion-based video retargeting with optimized crop-and-warp
ACM SIGGRAPH 2010 papers
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video summagator: an interface for video summarization and navigation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining Semantic Correlation of Heterogeneous Multimedia Data for Cross-Media Retrieval
IEEE Transactions on Multimedia
Video visualization for compact presentation and fast browsing of pictorial content
IEEE Transactions on Circuits and Systems for Video Technology
Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection
IEEE Transactions on Multimedia
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With the explosive growth of video resource, efficient techniques for generating video summarization are appealing for facilitating understanding and presenting video content. Traditional video summarizations were usually given through extracting key frames based on the features of frames in video sequence. However, in many cases, the given key frames don't meet the key frames reside in the mind of users. In this paper, we propose an innovative approach based on relevance feedback to select the key frames of a video sequence for video summarization considering users' subjective visual preference. A two-step strategy to select the key frames is given. 1) we evaluate the preference of user, and a Bayesian method is used to update the probability in condition of all the responses; 2) we take the interaction of the frames into consideration and select a proper frame set for video summarization. We verified that the relevance in different people's mind is not totally irrelevant. A relevance distance based on the characteristics of the video frames and the trend of users' decision making is proposed for more accurate likelihood definition. Experiments showed that our approach could provide satisfied summarizations in acceptable iteration in most cases and demonstrated the efficiency of the interactive and feedback process.