Baoquan Zhao1, Songhua Xu2, Shujin Lin3, Xiaonan Luo1, Lian Duan4. 1. National Engineering Research Center of Digital Life, School of Information Science and Technology, Sun Yat-sen University, Guangzhou, P.R. China. 2. Information Systems Department, New Jersey Institute of Technology, Newark, NJ, USA songhua.xu@njit.edu. 3. National Engineering Research Center of Digital Life, School of Information Science and Technology, Sun Yat-sen University, Guangzhou, P.R. China School of Communication and Design, Sun Yat-sen University, Guangzhou, P.R. China. 4. Department of Information Systems and Business Analytics, Hofstra University, NY, USA.
Abstract
OBJECTIVE: 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. MATERIALS AND METHODS: 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. CONCLUSION: 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.
OBJECTIVE: 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. MATERIALS AND METHODS: 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. CONCLUSION: 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.
Authors: Ralf P Botchen; Sven Bachthaler; Fabian Schick; Min Chen; Greg Mori; Daniel Weiskopf; Thomas Ertl Journal: IEEE Trans Vis Comput Graph Date: 2008 Jul-Aug Impact factor: 4.579
Authors: Kapil Chalil Madathil; A Joy Rivera-Rodriguez; Joel S Greenstein; Anand K Gramopadhye Journal: Health Informatics J Date: 2014-03-25 Impact factor: 2.681