A new visual navigation system for exploring biomedical Open Educational Resource (OER) 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.

[1]  Min Chen,et al.  Action-Based Multifield Video Visualization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[2]  Andreas Girgensohn,et al.  Stained-glass visualization for highly condensed video summaries , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[3]  Dianne Gregory,et al.  Descriptive analysis of YouTube music therapy videos. , 2011, Journal of music therapy.

[4]  Yuzhen Niu,et al.  Video summagator: an interface for video summarization and navigation , 2012, CHI.

[5]  Shingo Uchihashi,et al.  An interactive comic book presentation for exploring video , 2000, CHI.

[6]  Klaus Schöffmann,et al.  Video Browsing Using Interactive Navigation Summaries , 2009, 2009 Seventh International Workshop on Content-Based Multimedia Indexing.

[7]  Christoph Meinel,et al.  Content Based Lecture Video Retrieval Using Speech and Video Text Information , 2014, IEEE Transactions on Learning Technologies.

[8]  Donald A. Adjeroh,et al.  Adaptive Edge-Oriented Shot Boundary Detection , 2009, EURASIP J. Image Video Process..

[9]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[10]  Pourang Irani,et al.  Interactive Exploration of Surveillance Video through Action Shot Summarization and Trajectory Visualization , 2013, IEEE Transactions on Visualization and Computer Graphics.

[11]  Anand K. Gramopadhye,et al.  Healthcare information on YouTube: A systematic review , 2015, Health Informatics J..

[12]  Jin Liu,et al.  An Video Shot Segmentation Scheme Based on Adaptive Binary Searching and SIFT , 2011, ICIC.

[13]  Haibin Lu,et al.  A hierarchical organization scheme for video data , 2002, Pattern Recognit..

[14]  Priya Nambisan,et al.  Information seeking and social support in online health communities: impact on patients' perceived empathy , 2011, J. Am. Medical Informatics Assoc..

[15]  Cordelia Schmid,et al.  The AXES research video search system , 2014, ICASSP 2014.

[16]  Tiecheng Liu,et al.  An interactive system for video content exploration , 2006, IEEE Transactions on Consumer Electronics.

[17]  Olivier Buisson,et al.  Video exploration: from multimedia content analysis to interactive visualization , 2010, ACM Multimedia.

[18]  Jenny A Van Amburgh,et al.  A Vidcasting Project to Promote the Pharmacist's Role in Public Health , 2010, American Journal of Pharmaceutical Education.

[19]  Lucila Ohno-Machado,et al.  Reviewing social media use by clinicians , 2012, J. Am. Medical Informatics Assoc..

[20]  K. Murugiah,et al.  YouTube as a source of information on cardiopulmonary resuscitation. , 2011, Resuscitation.

[21]  Jeffrey Heer,et al.  D³ Data-Driven Documents , 2011, IEEE Transactions on Visualization and Computer Graphics.

[22]  Nancy Fjortoft,et al.  Impact of a Student Response System on Short- and Long-Term Learning in a Drug Literature Evaluation Course , 2010, American Journal of Pharmaceutical Education.

[23]  Raymond Smith,et al.  Adapting the Tesseract open source OCR engine for multilingual OCR , 2009, MOCR '09.

[24]  Paul Lamere,et al.  Sphinx-4: a flexible open source framework for speech recognition , 2004 .

[25]  Min Chen,et al.  Video visualization , 2003, IEEE Visualization, 2003. VIS 2003..