Content Based Lecture Video Retrieval Using Speech and Video Text Information
Content Based Lecture Video Retrieval Using Speech and Video Text Information
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DOI：
10.1109/tlt.2014.2307305
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发表时间：
2014-02
影响因子：
3.7
通讯作者：
Haojin Yang;C. Meinel
中科院分区：
文献类型：
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作者：
Haojin Yang;C. Meinel
在过去的十年里，电子讲座变得越来越受欢迎。万维网（WWW）上的讲座视频数据量正在迅速增长。因此，迫切需要一种更有效的方法在WWW或大型讲座视频档案中进行视频检索。本文提出了一种大型讲座视频档案的自动视频索引和视频检索方法。首先，采用自动视频分割和关键帧检测技术，为视频内容导航提供视觉指导。随后，我们通过在关键帧上应用视频光学字符识别（OCR）技术和在讲座音轨上应用自动语音识别（ASR）技术提取文本元数据。采用OCR和ASR文本以及检测到的幻灯片文本行类型进行关键字提取，提取视频级和段级关键字，用于基于内容的视频浏览和搜索。通过评价证明了所提出的索引功能的性能和有效性。
In the last decade e-lecturing has become more and more popular. The amount of lecture video data on the World Wide Web (WWW) is growing rapidly. Therefore, a more efficient method for video retrieval in WWW or within large lecture video archives is urgently needed. This paper presents an approach for automated video indexing and video search in large lecture video archives. First of all, we apply automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. Subsequently, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames and Automatic Speech Recognition (ASR) on lecture audio tracks. The OCR and ASR transcript as well as detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance and the effectiveness of proposed indexing functionalities is proven by evaluation.