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VideoPuzzle: Descriptive One-Shot Video Composition

delete2013-04-01
delete11
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OA
AI
陈
陈强 (Qiang Chen) *
王
王萌 (Meng Wang)
杨
杨华 (Hua Yang)
S
Song Zheng
YAN Shuicheng 封面图
YAN Shuicheng (Shuicheng Yan)
DOI:10.1109/TMM.2012.2236306delete
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摘要

摘要

En 中文
A large amount of short, single-shot videos are created by personal camcorder every day, such as the small video clips in family albums, and thus a solution for presenting and managing these video clips is highly desired. From the perspective of professionalism and artistry, long-take/shot video, also termed one-shot video, is able to present events, persons or scenic spots in an informative manner. This paper presents a novel video composition system Video Puzzle which generates aesthetically enhanced long-shot videos from short video clips. Our task here is to automatically composite several related single shots into a virtual long-take video with spatial and temporal consistency. We propose a novel framework to compose descriptive long-take video with content-consistent shots retrieved from a video pool. For each video, frame-by-frame search is performed over the entire pool to find start-end content correspondences through a coarse-to-fine partial matching process. The content correspondence here is general and can refer to the matched regions or objects, such as human body and face. The content consistency of these correspondences enables us to design several shot transition schemes to seamlessly stitch one shot to another in a spatially and temporally consistent manner. The entire long-take video thus comprises several single shots with consistent contents and fluent transitions. Meanwhile, with the generated matching graph of videos, the proposed system can also provide an efficient video browsing mode. Experiments are conducted on multiple video albums and the results demonstrate the effectiveness and the usefulness of the proposed scheme.
Keyword:
Image retrieval
one-shot video
video authoring
video transition
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期刊

IEEE Transactions on Multimedia 封面图
IEEE Transactions on Multimedia
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4.6K
被引数:
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hefei university of technology
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panasonic
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National University of Singapore
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