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Aesthetics-Guided Summarization from Multiple User Generated Videos

机译:多个用户生成的视频的美学指导总结

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摘要

In recent years, with the rapid development of camera technology and portable devices, we have witnessed a flourish of user generated videos, which are gradually reshaping the traditional professional video oriented media market. The volume of user generated videos in repositories is increasing at a rapid rate. In today's video retrieval systems, a simple query will return many videos which seriously increase the viewing burden. To manage these video retrievals and provide viewers with an efficient way to browse, we introduce a system to automatically generate a summarization from multiple user generated videos and present their salience to viewers in an enjoyable manner. Among multiple consumer videos, we find their qualities to be highly diverse due to various factors such as a photographer's experience or environmental conditions at the time of capture. Such quality inspires us to include a video quality evaluation component into the video summarization since videos with poor qualities can seriously degrade the viewing experience. We first propose a probabilistic model to evaluate the aesthetic quality of each user generated video. This model compares the rich aesthetics information from several well-known photo databases with generic unlabeled consumer videos, under a human perception component indicating the correlation between a video and its constituting frames. Subjective studies were carried out with the results indicating that our method is reliable. Then a novel graph-based formulation is proposed for the multi-video summarization task. Desirable summarization criteria is incorporated as the graph attributes and the problem is solved through a dynamic programming framework. Comparisons with several state-of-the-art methods demonstrate that our algorithm performs better than other methods in generating a skimming video in preserving the essential scenes from the original multiple input videos, with smooth transitions among consecutive segments and appealing aesthetics overall.
机译:近年来,随着相机技术和便携式设备的飞速发展,我们见证了用户生成的视频的兴旺发展,这些视频正在逐步重塑传统的面向专业视频的媒体市场。用户在存储库中生成的视频的数量正在迅速增加。在当今的视频检索系统中,简单的查询将返回许多视频,这严重增加了观看负担。为了管理这些视频检索并为观众提供一种有效的浏览方式,我们引入了一种系统,该系统可以自动从多个用户生成的视频中生成摘要,并以令人愉悦的方式向观众展示其显着性。在多个消费者视频中,由于各种因素(例如摄影师的拍摄经验或拍摄时的环境条件),我们发现它们的质量差异很大。这种质量激励我们在视频摘要中包含视频质量评估组件,因为质量差的视频会严重降低观看体验。我们首先提出一个概率模型来评估每个用户生成的视频的美学质量。该模型在表示视频及其构成帧之间的相关性的人类感知成分下,将来自几个知名照片数据库的丰富美学信息与未标记的通用消费者视频进行了比较。进行了主观研究,结果表明我们的方法是可靠的。然后针对多视频摘要任务提出了一种新颖的基于图的表示方法。理想的汇总标准作为图形属性被合并,并且该问题通过动态编程框架得以解决。与几种最先进方法的比较表明,在保留原始多个输入视频的基本场景时,我们的算法在生成撇取视频方面表现出比其他方法更好的效果,并且在连续段之间可以平滑过渡,并具有整体吸引力。

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