  Video summarization has unprecedented importance to help us digest, browse,
and search today's ever-growing video collections. We propose a novel subset
selection technique that leverages supervision in the form of human-created
summaries to perform automatic keyframe-based video summarization. The main
idea is to nonparametrically transfer summary structures from annotated videos
to unseen test videos. We show how to extend our method to exploit semantic
side information about the video's category/genre to guide the transfer process
by those training videos semantically consistent with the test input. We also
show how to generalize our method to subshot-based summarization, which not
only reduces computational costs but also provides more flexible ways of
defining visual similarity across subshots spanning several frames. We conduct
extensive evaluation on several benchmarks and demonstrate promising results,
outperforming existing methods in several settings.
