An intuitive and efficient access interface to real-time incoming video based on automatic indexing
Proceedings of the third ACM international conference on Multimedia
PanoramaExcerpts: extracting and packing panoramas for video browsing
MULTIMEDIA '97 Proceedings of the fifth 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)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Applications of Video-Content Analysis and Retrieval
IEEE MultiMedia
Hierarchical Shot Clustering for Video Summarization
ICCS '02 Proceedings of the International Conference on Computational Science-Part III
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The Journal of Machine Learning Research
Key frame selection by motion analysis
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Extracting story units from long programs for video browsing and navigation
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Non-sequential video content representation using temporal variation of feature vectors
IEEE Transactions on Consumer Electronics
Summarization of videotaped presentations: automatic analysis of motion and gesture
IEEE Transactions on Circuits and Systems for Video Technology
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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In this paper, we present a scalable keyframe extraction method using one-class support vector machine. Keyframe extraction seeks to generate "good" images that best represent underlying video content and provide content-based access points. Criteria for "good" images play a major role for keyframe extraction process. Extracting "good images" can be viewed as detecting "novel images" among all the frames within a video. Therefore, keyframe extraction reduces to novelty detection problem. We describe how to efficiently solve the novelty detection problem using one-class support vector machine. We also present an algorithm of extracting keyframes in a scalable way so that one can access a video from coarse to fine resolution. We demonstrate the performance of our algorithm on several different types of videos.