Zhao Baoquan, Xu Songhua, Lin Shujin, Luo Xiaonan, Duan Lian
National Engineering Research Center of Digital Life, School of Information Science and Technology, Sun Yat-sen University, Guangzhou, P.R. China.
Information Systems Department, New Jersey Institute of Technology, Newark, NJ, USA
J Am Med Inform Assoc. 2016 Apr;23(e1):e34-41. doi: 10.1093/jamia/ocv123. Epub 2015 Sep 2.
Biomedical videos as open educational resources (OERs) are increasingly proliferating on the Internet. Unfortunately, seeking personally valuable content from among the vast corpus of quality yet diverse OER videos is nontrivial due to limitations of today's keyword- and content-based video retrieval techniques. To address this need, this study introduces a novel visual navigation system that facilitates users' information seeking from biomedical OER videos in mass quantity by interactively offering visual and textual navigational clues that are both semantically revealing and user-friendly.
The authors collected and processed around 25 000 YouTube videos, which collectively last for a total length of about 4000 h, in the broad field of biomedical sciences for our experiment. For each video, its semantic clues are first extracted automatically through computationally analyzing audio and visual signals, as well as text either accompanying or embedded in the video. These extracted clues are subsequently stored in a metadata database and indexed by a high-performance text search engine. During the online retrieval stage, the system renders video search results as dynamic web pages using a JavaScript library that allows users to interactively and intuitively explore video content both efficiently and effectively.ResultsThe authors produced a prototype implementation of the proposed system, which is publicly accessible athttps://patentq.njit.edu/oer To examine the overall advantage of the proposed system for exploring biomedical OER videos, the authors further conducted a user study of a modest scale. The study results encouragingly demonstrate the functional effectiveness and user-friendliness of the new system for facilitating information seeking from and content exploration among massive biomedical OER videos.
Using the proposed tool, users can efficiently and effectively find videos of interest, precisely locate video segments delivering personally valuable information, as well as intuitively and conveniently preview essential content of a single or a collection of videos.
生物医学视频作为开放教育资源（OER）在互联网上日益增多。遗憾的是，由于当今基于关键词和内容的视频检索技术的局限性，要从大量高质量且多样的OER视频中找到个人有价值的内容并非易事。为满足这一需求，本研究引入了一种新颖的视觉导航系统，通过交互式提供语义丰富且用户友好的视觉和文本导航线索，帮助用户从大量生物医学OER视频中查找信息。
作者收集并处理了约25000个YouTube视频用于实验，这些视频涵盖生物医学科学广泛领域，总时长约4000小时。对于每个视频，首先通过对音频、视觉信号以及视频附带或嵌入的文本进行计算分析，自动提取其语义线索。随后将这些提取的线索存储在元数据库中，并由高性能文本搜索引擎进行索引。在在线检索阶段，系统使用JavaScript库将视频搜索结果呈现为动态网页，允许用户高效且有效地交互式直观探索视频内容。
作者制作了所提出系统的原型实现，可通过https://patentq.njit.edu/oer公开访问。为检验所提出系统在探索生物医学OER视频方面的整体优势，作者进一步进行了小规模用户研究。研究结果令人鼓舞地证明了新系统在促进从大量生物医学OER视频中查找信息和内容探索方面的功能有效性和用户友好性。
使用所提出的工具，用户可以高效且有效地找到感兴趣的视频，精确定位传递个人有价值信息的视频片段，以及直观方便地预览单个或一组视频的重要内容。
