IEEE Trans Image Process. 2017 Oct;26(10):4712-4724. doi: 10.1109/TIP.2017.2708902. Epub 2017 May 26.
Most video summarization approaches have focused on extracting a summary from a single video; we propose an unsupervised framework for summarizing a collection of videos. We observe that each video in the collection may contain some information that other videos do not have, and thus exploring the underlying complementarity could be beneficial in creating a diverse informative summary. We develop a novel diversity-aware sparse optimization method for multi-video summarization by exploring the complementarity within the videos. Our approach extracts a multi-video summary, which is both interesting and representative in describing the whole video collection. To efficiently solve our optimization problem, we develop an alternating minimization algorithm that minimizes the overall objective function with respect to one video at a time while fixing the other videos. Moreover, we introduce a new benchmark data set, Tour20, that contains 140 videos with multiple manually created summaries, which were acquired in a controlled experiment. Finally, by extensive experiments on the new Tour20 data set and several other multi-view data sets, we show that the proposed approach clearly outperforms the state-of-the-art methods on the two problems-topic-oriented video summarization and multi-view video summarization in a camera network.
大多数视频摘要方法都集中于从单个视频中提取摘要；我们提出了一个无监督的框架，用于对多个视频进行摘要。我们观察到，集合中的每个视频可能包含其他视频没有的一些信息，因此探索潜在的互补性可能有助于创建多样化的信息丰富的摘要。我们通过探索视频内的互补性，开发了一种新颖的多视频摘要方法，该方法通过探索视频内的互补性来提取多视频摘要，该摘要在描述整个视频集合时既有趣又具有代表性。为了有效地解决我们的优化问题，我们开发了一种交替最小化算法，该算法每次固定其他视频，仅对一个视频最小化整体目标函数。此外，我们引入了一个新的基准数据集 Tour20，其中包含 140 个视频，每个视频都有多个由人工创建的摘要，这些摘要是在受控实验中获得的。最后，通过在新的 Tour20 数据集和其他几个多视图数据集上进行广泛的实验，我们表明，该方法在面向主题的视频摘要和相机网络中的多视图视频摘要这两个问题上明显优于最先进的方法。
