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Cluster Computing

, Volume 22, Supplement 1, pp 999–1009 | Cite as

Customized video service system design and implementation: from taste to image-based consuming method

  • Hyojin Park
  • Jinhong Yang
  • Hyuncheol KimEmail author
  • Jun Kyun ChoiEmail author
Article
  • 68 Downloads

Abstract

To deliver the myriad of videos tailored to each user, so far, most of the video services have provided customization service by video recommendations. However, as the place, device, and application for consuming video become almost anywhere, customization not only for users’ taste but also for their video consuming environment is required. In this paper, we propose a video service system which can capture users’ preferences and serve videos with both original and image based forms in a customized way. The image based form enables users’ to ‘read’ the video’s content by providing multiple keyframe images in a carousel form with full script of the video and works as the index of the video as well. It facilitates the video to be more easily linked and used in other services while diminishing playtime, data traffic, and sound restrictions. Through the proposed system implementation and service operation, we were able to confirm that the service use time and content consumption of returning visitors promoted by 2.5 times longer and 2.39 more in average compared to the first-time visitors.

Keywords

Customized video service Metadata processing Natural language processing Image carousels 

References

  1. 1.
    Statista: Hours of video uploaded to YouTube every minute as of July 2015 (2015). http://www.statista.com/statistics/259477/hours-of-video-uploaded-to-youtube-every-minute/. Accessed 3 April 2017
  2. 2.
    Wen, Yonggang, Han, Hu, Liu, Fang: Embracing social big data in wireless system design. J. Commun. Inf. Netw. 2(1), 81–96 (2017)CrossRefGoogle Scholar
  3. 3.
    Amatriain, X., Basilico, J.: Recommender systems in industry: a Netflix Case Study. In: Recommender Systems Handbook, pp. 385–419. Springer, New York (2015)Google Scholar
  4. 4.
    Lekakos, G., Chambel, T., Knoche, H.: Special issue on social recommendation and delivery systems for video and TV content. Multimed. Syst. 19(6), 475–476 (2013)CrossRefGoogle Scholar
  5. 5.
    Blasch, E.P., et al.: QuEST for information fusion in multimedia reports. Int. J. Monit. Surveill. Technol. Res. (IJMSTR) 2(3), 1–30 (2014)Google Scholar
  6. 6.
    Hmedeh, Z., Kourdounakis, H., Christophides, V., et al.: Content-based publish/subscribe system for web syndication. J. Comput. Sci. Technol. 31(2), 359–380 (2016)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Yang, J., Park, H., Lee, G.M., Choi, J.K.: A web-based IPTV content syndication system for personalized content guide. J. Commun. Netw. 17(1), 67–74 (2015)CrossRefGoogle Scholar
  8. 8.
    Aved, A.J., et al.: Multi-INT query language for DDDAS designs. Procedia Comput. Sci. 51, 2518–2523 (2015)CrossRefGoogle Scholar
  9. 9.
    Lew, M.S.: Content-based multimedia information retrieval: state of the art and challenge. ACM Trans. Multimed. Comput. Commun. Appl. 2(1), 1–19 (2006)CrossRefGoogle Scholar
  10. 10.
    Kaiser, R., Hausenblas, M., Umgeher, M.: Metadata-driven interactive web video assembly. Multimed. Tools Appl. 41(3), 437–467 (2009)CrossRefGoogle Scholar
  11. 11.
    85 percent of Facebook video is watched without sound (2017). http://digiday.com/media/silent-world-facebook-video, digiday
  12. 12.
    Yang, J., Park, H., Jeon, K., Jeong, J., Choi, J.K.: Serving a video into an image carousel: system design and implementation. Cluster Comput. (2016)Google Scholar
  13. 13.
    Park H., Han K., Yang J., Choi J.K.: Enhanced metadata creation and utilization for personalized IPTV service. In: Lecture Notes in Electrical Engineering, vol 424. Springer (2017)Google Scholar
  14. 14.
    Datta, R., Li, J., Wang, J.Z.: Content-based image retrieval: approaches and trends of the new age. In: Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 253–262 (2005)Google Scholar
  15. 15.
    Google Cloud Speech API. https://cloud.google.com/speech/. Accessed 28 May 2017
  16. 16.
    YouTube, Automatic Captioning. https://support.google.com/youtube/answer/6373554. Accessed 28 May 2017
  17. 17.
    Twitter Korean Text. https://github.com/twitter/twitter-korean-text. Accessed 1 April 2017
  18. 18.
    Vijayakumar, V., Nedunchezhian, R.: A study on video data mining. Int. J. Multimed. Inf Retr. 1(3), 153–172 (2012)CrossRefGoogle Scholar
  19. 19.
    Oh, J., Bandi, B.: Multimedia data mining framework for raw video sequences. In: Proceedings of the Third International Workshop on Multimedia Data Mining (MDM/KDD 2002), pp. 18–35 (2002)Google Scholar
  20. 20.
    Bhatt, C.A., Kankanhalli, M.S.: Multimedia data mining: state of the art and challenges. Multimed. Tools Appl. 51(1), 35–76 (2011)CrossRefGoogle Scholar
  21. 21.
    Naaman, M.: Social multimedia: highlighting opportunities for search and mining of multimedia data in social media applications. Multimed. Tools Appl. 56(1), 9–34 (2012)CrossRefGoogle Scholar
  22. 22.
    Yahiaoui, I., Merialdo, B., Huet, B.: Comparison of multiepisode video summarization algorithms. EURASIP J. Adv. Signal Process. 2003, 48–55 (2003)CrossRefzbMATHGoogle Scholar
  23. 23.
    Wu, J., Zhong, S., Jiang, J., et al.: A novel clustering method for static video summarization. Multimed. Tools Appl. 76(7), 9625–9641 (2017)CrossRefGoogle Scholar
  24. 24.
    Zhu, X., Wu, X., Fan, J., et al.: Exploring video content structure for hierarchical summarization. Multimed. Syst. 10(2), 98–115 (2004)CrossRefGoogle Scholar
  25. 25.
    Chen, S.N.: Storyboard-based accurate automatic summary video editing system. Multimed. Tools Appl. (2016)Google Scholar
  26. 26.
    FFMPEG. https://ffmpeg.org/. Accessed 29 May 2017
  27. 27.
    Google Analytics Solutions. https://www.google.com/analytics/. Accessed 28 May 2017

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Information and CommunicationsKAISTDaejonKorea
  2. 2.R&D TeamHECAS IncSeoulKorea
  3. 3.School of Electrical EngineeringKAISTDaejonKorea
  4. 4.Department of Convergence ScienceNamseoul UniversityCheonanKorea

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