So, how to do video annotation efficiently is the purpose of this paper.
因此，如何进行有效的视频语义标注是本文研究的目的。
The automatic video annotation is a key module of the video indexing and retrieval system, and the extraction and location of captions are important steps for video annotation.
视频的自动标注是视频检索系统中的重要模块，而字幕的提取与定位则是视频标注的重要步骤。
The accuracy of his algorithm demonstrates promise in improving video and still-image annotation, advertising, and the monitoring and identification of people appearing in public.
他的算法的精确度，为视频和静态影像注释、广告，以及监视和识别公共场所的人流等方面带来进步的希望。
At present, most of video semantic annotation methods are based on statistic theory. The methods use supervised learning method to do semantic label.
目前已有的视频语义标注方法多是基于统计学理论，采用全监督学习方法进行语义标注工作。
The semantic retrieval of news video is implemented by the annotation of scene through metadata annotation.
通过引入元数据的标注完成对场景的标注，实现用户的语义查询。
In this paper, we discuss the problem of using semi-supervised learning method to do video semantic annotation.
本文讨论了利用半监督学习方法进行视频语义标注的问题。
In this paper, we discuss the problem of using semi-supervised learning method to do video semantic annotation.
本文讨论了利用半监督学习方法进行视频语义标注的问题。
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