Summary Transfer: Exemplar-based Subset Selection for Video Summarization
This addresses the problem of efficiently digesting, browsing, and searching large video collections for users, representing an incremental improvement with novel method elements.
The paper tackles video summarization by proposing a novel subset selection technique that transfers summary structures from annotated videos to unseen test videos, demonstrating promising results that outperform existing methods on several benchmarks.
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.