Summarization of wearable videos using support vector machine
Summarization of wearable videos using support vector machine
复制标题
使用支持向量机总结可穿戴视频
DOI：
10.1109/icme.2002.1035784
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
K. Aizawa
中科院分区：
文献类型：
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作者：
Haung Wei Ng;Y. Sawahata;K. Aizawa
随着多媒体内容的不断增长，视频内容的自动摘要已经成为一个重要的课题。这方面的研究表明，低级别的视频和音频特征在分类视频内容的有效性。利用脑电波来反映个人兴趣也被证明是实用的。在本文中，我们使用支持向量机（SVM）的音频/视频功能和脑电波（/spl α/-波）之间的关系建模。基于支持向量机模型，我们总结了可穿戴视频的个人兴趣，仅使用低级别的视频和音频特征。在这里，我们将“可穿戴视频”定义为使用可穿戴摄像机和计算机连续记录个人体验。我们的实验结果表明，超过90%的准确率总结的25分钟的视频剪辑与SVM模型创建的另一个类似的25分钟的视频剪辑。
Auto-summarization of video contents has become an important topic following the growing amount of multimedia contents. Researches in this area have shown the effectiveness of low-level video and audio features in categorizing video contents. The use of brainwaves to reflect personal interests is also proven to be practical. In this paper, we model the relationship between audio/video features and brainwaves (/spl alpha/-waves) using the support vector machine (SVM). Based on the SVM model, we summarized wearable videos by personal interests, using only low-level video and audio features. Here we define "wearable videos" as continuous recordings of personal experiences using wearable video camera and computer. Our experiment results showed over 90% of accuracy on summarization of a 25-minute video clip with an SVM model created by another resembling 25-minute video clip.
DOI：
10.1109/icip.2001.958135
发表时间：
2001-10
期刊：
Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205)
影响因子：
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作者：
K. Aizawa;Kenichiro Ishijima;Makoto Shiina
通讯作者：
K. Aizawa;Kenichiro Ishijima;Makoto Shiina