Summary Transfer: Exemplar-based Subset Selection for Video Summarization
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016)
摘要
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.
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关键词
summary transfer,exemplar-based subset selection,supervision leveraging,humancreated summaries,automatic keyframe-based video summarization,summary structure nonparametric transfer,video annotation,semantic side information,video category,video genre,videos semantic training,subshot-based summarization,visual similarity defining
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