Unearthing Latent Information Segments of Academic Videos on the Web

为了在Web上建立特定领域的Web规模的视频数字图书馆，它是至关重要的，能够识别和提取某些感兴趣的信息（称为信息段）有效和自动。例如，通过从网络上收集所谓的学术视频及其信息片段，人们可以建立一个类似于CiteSeer或Google Scholar的下一代数字图书馆。然而，它只对学术视频（而不是学术论文）进行存档和索引。为了实现这一目标，我们进行了初步的研究，以开发这样的识别和提取的潜在信息片段，从特定领域的视频在Web上。重点是如何从视频内容和下载视频的网页中挖掘各种元数据和相关数据。来自机器学习的技术（例如，LDA）、数据提取和集成（例如，包装器/中介器），自然语言处理（例如，命名实体识别和提取），以及多媒体处理（例如，近似重复检测）被适当地评估、应用和扩展。这种技术在大量视频数据上的可扩展性也在探索中。

In order to build domain-specific web-scale video digital libraries on the Web, it is critical to be able to identify and extract certain information of interest (termed information segments) efficiently and automatically. For instance, by collecting only so-called academic videos and their information segments from the Web, one can build a next-generation digital library similar to CiteSeer or Google Scholar. However, that only archives and indexes academic videos (instead of academic papers). Toward this goal, we conduct a preliminary study to develop such identification and extraction of latent information segments from domain-specific videos on the Web. Key emphasis is on how to unearth diverse metadata and associated data from video contents and web pages from which videos are downloaded. Techniques from machine learning (e.g., LDA), data extraction and integration (e.g., wrapper/mediator), natural language processing (e.g., named entity recognition and extraction), and multimedia processing (e.g., near-duplicate detection) are evaluated, applied, and extended appropriately. Scalability of such techniques over large volumes of video data is also being explored.