The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Video summarization by learning submodular mixtures of objectives
Open accessAbstract
We present a novel method for summarizing raw, casually captured videos. The objective is to create a short summary that still conveys the story. It should thus be both, interesting and representative for the input video. Previous methods often used simplified assumptions and only optimized for one of these goals. Alternatively, they used handdefined objectives that were optimized sequentially by making consecutive hard decisions. This limits their use to a particular setting. Instead, we introduce a new method that (i) uses a supervised approach in order to learn the importance of global characteristics of a summary and (ii) jointly optimizes for multiple objectives and thus creates summaries that posses multiple properties of a good summary. Experiments on two challenging and very diverse datasets demonstrate the effectiveness of our method, where we outperform or match current state-of-the-art.
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Gygli, M., Grabner, H., Riemenschneider, H., & Van Gool, L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_33 · Cited by 752
Self-citationSubmodular Function Maximization
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Discovering important people and objects for egocentric video summarization
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Mahasseni, B., Lam, M., & Todorovic, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.318 · Cited by 607
Zhou, K., Qiao, Y., & Xiang, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12255 · Cited by 473
Video Summarization Using Fully Convolutional Sequence Networks
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Metadata
- Authors
- Gygli, M., Grabner, H., & Van Gool, L.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 3090-3098
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1904325426
- Cited by
- 397
- References
- 39
- FWCI
- 17.32
- Type
- conference paper
- Language
- en
- Open access
- green
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- Gygli, M., Grabner, H., & Van Gool, L. (2015). Video summarization by learning submodular mixtures of objectives. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298928
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