${mbi{L}}_{bf 2,1}$ optimization model, which simultaneously characterizes the reconstruction capabili'/> Diversified Key-Frame Selection Using Structured <inline-formula><tex-math notation='TeX'>${L_{2,1}}$</tex-math></inline-formula> Optimization
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Diversified Key-Frame Selection Using Structured ${L_{2,1}}$ Optimization

机译：使用结构化 $ {L_ {2,1}} $ 优化的多种关键帧选择

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摘要

In this paper, a structured ${mbi{L}}_{bf 2,1}$ optimization model, which simultaneously characterizes the reconstruction capability and diversity, is proposed to provide a semantically meaningful representation of a short video clip acquired from digital cameras or a mobile robot. In this model, a mutual inhabitation penalty term is imposed to prevent similar samples from being selected simultaneously. The proposed model is highly flexible to incorporate different mutual inhabitation terms and the temporal redundancy in video is exploited to encourage the diversity. The constructed objective function is nonconvex and an iterative algorithm is developed to solve the optimization problem. The performance is evaluated using various video clips from YouTube and also based on practical video captured by an indoor mobile robot. The results clearly indicate that the proposed strategy helps the optimization model to achieve more diversified key frames than the other existing work method.
机译：在本文中，结构化 $ {mbi {L}} _ {bf 2,1} $ 优化模型为了同时描述从数字相机或移动机器人获取的短视频剪辑，提出了语义上有意义的表示形式，它同时表征了重建能力和多样性。在此模型中，施加了一个共同居住惩罚项，以防止同时选择相似的样本。所提出的模型具有很高的灵活性，可以合并不同的相互居住条件，并且利用视频中的时间冗余来鼓励多样性。构造的目标函数是非凸的，并开发了迭代算法来解决优化问题。使用YouTube上的各种视频片段以及室内移动机器人捕获的实际视频来评估性能。结果清楚地表明，与现有的其他工作方法相比，所提出的策略有助于优化模型获得更多的关键帧。

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