A magnifier tool for video data
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An intuitive and efficient access interface to real-time incoming video based on automatic indexing
Proceedings of the third ACM international conference on Multimedia
Video Manga: generating semantically meaningful video summaries
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Auto-summarization of audio-video presentations
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
The budgeted maximum coverage problem
Information Processing Letters
Readings in multimedia computing and networking
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Coaching variables for regression and classification
Statistics and Computing
Using Audio Time Scale Modification for Video Browsing
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 3 - Volume 3
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 3 - Volume 3
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Automated generation of news content hierarchy by integrating audio, video, and text information
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Generating comprehensible summaries of rushes sequences based on robust feature matching
Proceedings of the international workshop on TRECVID video summarization
Video rushes summarization by adaptive acceleration and stacking of shots
Proceedings of the international workshop on TRECVID video summarization
Predicting diverse subsets using structural SVMs
Proceedings of the 25th international conference on Machine learning
Enhancing diversity, coverage and balance for summarization through structure learning
Proceedings of the 18th international conference on World wide web
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Mapping and revising Markov logic networks for transfer learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
IEEE Transactions on Multimedia - Special issue on integration of context and content
Automated video program summarization using speech transcripts
IEEE Transactions on Multimedia
Video visualization for compact presentation and fast browsing of pictorial content
IEEE Transactions on Circuits and Systems for Video Technology
Summarization of videotaped presentations: automatic analysis of motion and gesture
IEEE Transactions on Circuits and Systems for Video Technology
Video summarization and scene detection by graph modeling
IEEE Transactions on Circuits and Systems for Video Technology
Is that scene dangerous?: transferring knowledge over a video stream
Proceedings of the 5th Ph.D. workshop on Information and knowledge
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It is well-known that textual information such as video transcripts and video reviews can significantly enhance the performance of video summarization algorithms. Unfortunately, many videos on the Web such as those from the popular video sharing site YouTube do not have useful textual information. The goal of this paper is to propose a transfer learning framework for video summarization: in the training process both the video features and textual features are exploited to train a summarization algorithm while for summarizing a new video only its video features are utilized. The basic idea is to explore the transferability between videos and their corresponding textual information. Based on the assumption that video features and textual features are highly correlated with each other, we can transfer textual information into knowledge on summarization using video information only. In particular, we formulate the video summarization problem as that of learning a mapping from a set of shots of a video to a subset of the shots using the general framework of SVM-based structured learning. Textual information is transferred by encoding them into a set of constraints used in the structured learning process which tend to provide a more detailed and accurate characterization of the different subsets of shots. Experimental results show significant performance improvement of our approach and demonstrate the utility of textual information for enhancing video summarization.