  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 dataset, Tour20, that contains 140 videos with
multiple human created summaries, which were acquired in a controlled
experiment. Finally, by extensive experiments on the new Tour20 dataset and
several other multi-view datasets, 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.
