Affective computing
Communications of the ACM
Evolving video skims into useful multimedia abstractions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Signal Processing - Video segmentation for content-based processing manipulation
Video summarization by curve simplification
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
Visual digests for news video libraries
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
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)
Dynamic video summarization and visualization
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Video summarisation based on the psychological content in the track structure
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Audio as a Support to Scene Change Detection and Characterization of Video Sequences
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
Performance characterization of video-shot-change detection methods
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)
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The abstraction of a long video is often useful to a user in determining whether the video is worth viewing or not. In particular, video abstraction provides users of digital libraries with a fast, safe and reliable access of video data. Two approaches, summary sequences and highlights, are possible in video abstraction. The summary sequences are good for documentaries because they give an overview of the contents of the entire video, whereas highlights are good for movie trailers because they contain only the most interesting video segments. The video abstraction can be generated by three steps: analyzing video, selecting video clips, and synthesizing the output. In the analyzing video step, salient features, structures, or patterns in visual information, audio information, and textual information are detected. In the selecting step, meaningful clips are selected from detected features in the previous step. In the output synthesis step, the selected video clips are composed into the final form of the abstract. In this chapter, we will discuss various video abstraction techniques for digital libraries. In addition, we will also discuss a context-based video abstraction method in which contextual information of the video shot is computed. This method is useful in generating highlights because the contextual information of the video shot reflects semantics in video data.