Automatic video summarization by graph modeling

C. Ngo, Yu-Fei Ma, HongJiang Zhang
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引用次数: 155

Abstract

We propose a unified approach for summarization based on the analysis of video structures and video highlights. Our approach emphasizes both the content balance and perceptual quality of a summary. Normalized cut algorithm is employed to globally and optimally partition a video into clusters. A motion attention model based on human perception is employed to compute the perceptual quality of shots and clusters. The clusters, together with the computed attention values, form a temporal graph similar to Markov chain that inherently describes the evolution and perceptual importance of video clusters. In our application, the flow of a temporal graph is utilized to group similar clusters into scenes, while the attention values are used as guidelines to select appropriate subshots in scenes for summarization.
基于图形建模的自动视频摘要
在分析视频结构和视频亮点的基础上,提出了一种统一的视频摘要方法。我们的方法强调内容平衡和摘要的感知质量。采用归一化切割算法对视频进行全局最优的聚类划分。采用基于人类感知的运动注意模型来计算镜头和簇的感知质量。这些聚类与计算出的关注值一起,形成了一个类似于马尔可夫链的时间图,它本质上描述了视频聚类的演变和感知重要性。在我们的应用程序中,时序图的流被用来将相似的集群分组到场景中,而注意力值被用作在场景中选择适当的子镜头进行总结的指南。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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