  While most existing video summarization approaches aim to extract an
informative summary of a single video, we propose a novel framework for
summarizing multi-view videos by exploiting both intra- and inter-view content
correlations in a joint embedding space. We learn the embedding by minimizing
an objective function that has two terms: one due to intra-view correlations
and another due to inter-view correlations across the multiple views. The
solution can be obtained directly by solving one Eigen-value problem that is
linear in the number of multi-view videos. We then employ a sparse
representative selection approach over the learned embedding space to summarize
the multi-view videos. Experimental results on several benchmark datasets
demonstrate that our proposed approach clearly outperforms the
state-of-the-art.
