利用二部图对视频进行自动文摘的研究Automatic Summarization of Rushes Video using Bipartite Graphs |
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课程网址: | http://videolectures.net/samt08_smeaton_asrv/ |
主讲教师: | Alan F. Smeaton |
开课单位: | 都柏林城市大学 |
开课时间: | 2008-12-18 |
课程语种: | 英语 |
中文简介: | 在本文中,我们提出了一种新的方法来自动总结冲刺,或非结构化视频。我们的方法由三个主要步骤组成。首先,基于镜头和子镜头分段,我们过滤信息含量低的子镜头,这些子镜头在总结中不太可能有用。其次,采用二部图中最大匹配的方法来测量剩余镜头之间的相似度,并通过删除拉什视频中常见的重复重拍镜头来最小化镜头间冗余度。最后,在每个子镜头中都描述了面的存在和运动强度。在此基础上,提出了子镜头在整个视频环境中的代表性度量。然后生成由关键帧幻灯片组成的视频摘要。为了评估这种方法的有效性,我们重新运行了Trec进行的评估,使用2007年TrecVid视频总结任务中使用的相同数据集和评估指标,但使用了我们自己的评估人员。结果表明,我们的方法在Trecvid摘要基本事实部分方面有显著改进,并且与Trecvid 2007中的其他方法具有竞争力。 |
课程简介: | In this paper we present a new approach for automatic summarization of rushes, or unstructured video. Our approach is composed of three major steps. First, based on shot and sub-shot segmentations, we filter sub-shots with low information content not likely to be useful in a summary. Second, a method using maximal matching in a bipartite graph is adapted to measure similarity between the remaining shots and to minimize inter-shot redundancy by removing repetitive retake shots common in rushes video. Finally, the presence of faces and motion intensity are characterised in each sub-shot. A measure of how representative the sub-shot is in the context of the overall video is then proposed. Video summaries composed of keyframe slideshows are then generated. In order to evaluate the effectiveness of this approach we re-run the evaluation carried out by TREC, using the same dataset and evaluation metrics used in the TRECVID video summarization task in 2007 but with our own assessors. Results show that our approach leads to a significant improvement in terms of the fraction of the TRECVID summary ground truth included and is competitive with other approaches in TRECVID 2007. |
关 键 词: | 数据集; 视频摘要; 任务 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-02:张荧(课程编辑志愿者) |
阅读次数: | 28 |