Abstract
Video summarization has become one of the most effective solutions for quickly understanding a large amount of video data. Video properties such as importance, diversity, representativeness and storyness have been widely adopted for summarization based on kinds of features of video frames. To fully exploit these properties, in this paper we propose a property constrained video summarization framework to output fixed-size summaries based on the concept of regret minimization which is popular in the database community for solving multi-criteria decision making problems.
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Xu, Y., Zheng, J., Tao, Y., Zhu, K. (2024). A Property Constrained Video Summarization Framework via Regret Minimization. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14325. Springer, Singapore. https://doi.org/10.1007/978-981-99-7019-3_28
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DOI: https://doi.org/10.1007/978-981-99-7019-3_28
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