Multi-video summarization based on Video-MMR
Multi-video summarization based on Video-MMR
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发表时间：
2010-04
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通讯作者：
Yingbo Li;B. Mérialdo
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作者：
Yingbo Li;B. Mérialdo
本文提出了一种新的有效的多视频摘要方法：视频最大边缘相关（Video-MMR），它扩展了文本摘要的经典算法，最大边缘相关。Video-MMR奖励相关关键帧并惩罚冗余关键帧，就像MMR对文本片段所做的那样。两个变种的视频MMR的建议，我们提出了一个标准来选择最佳的参数组合的视频MMR。然后，我们比较了两种摘要策略：全局摘要，它同时汇总所有单独的视频，以及单独摘要，它独立地汇总每个单独的视频并连接结果。最后，将Video-MMR算法与目前流行的K-means算法进行了比较，并给出了用户总结。
This paper presents a novel and effective approach for multi-video summarization: Video Maximal Marginal Relevance (Video-MMR), which extends a classical algorithm of text summarization, Maximal Marginal Relevance. Video-MMR rewards relevant keyframes and penalizes redundant keyframes, as MMR does with text fragments. Two variants of Video-MMR are suggested, and we propose a criterion to select the best combination of parameters for Video-MMR. Then, we compare two summarization strategies: Global Summarization, which summarizes all the individual videos at the same time, and Individual Summarization, which summarizes each individual video independently and concatenates the results. Finally, Video-MMR algorithm is compared with popular K-means algorithm, supported by user-made summary.