Skip to main content
Log in

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

  • Published:
Cluster Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  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. Wen, Yonggang, Han, Hu, Liu, Fang: Embracing social big data in wireless system design. J. Commun. Inf. Netw. 2(1), 81–96 (2017)

    Article  Google Scholar 

  3. Amatriain, X., Basilico, J.: Recommender systems in industry: a Netflix Case Study. In: Recommender Systems Handbook, pp. 385–419. Springer, New York (2015)

  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)

    Article  Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  8. Aved, A.J., et al.: Multi-INT query language for DDDAS designs. Procedia Comput. Sci. 51, 2518–2523 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  10. Kaiser, R., Hausenblas, M., Umgeher, M.: Metadata-driven interactive web video assembly. Multimed. Tools Appl. 41(3), 437–467 (2009)

    Article  Google Scholar 

  11. 85 percent of Facebook video is watched without sound (2017). http://digiday.com/media/silent-world-facebook-video, digiday

  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)

  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)

  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)

  15. Google Cloud Speech API. https://cloud.google.com/speech/. Accessed 28 May 2017

  16. YouTube, Automatic Captioning. https://support.google.com/youtube/answer/6373554. Accessed 28 May 2017

  17. Twitter Korean Text. https://github.com/twitter/twitter-korean-text. Accessed 1 April 2017

  18. Vijayakumar, V., Nedunchezhian, R.: A study on video data mining. Int. J. Multimed. Inf Retr. 1(3), 153–172 (2012)

    Article  Google Scholar 

  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)

  20. Bhatt, C.A., Kankanhalli, M.S.: Multimedia data mining: state of the art and challenges. Multimed. Tools Appl. 51(1), 35–76 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  22. Yahiaoui, I., Merialdo, B., Huet, B.: Comparison of multiepisode video summarization algorithms. EURASIP J. Adv. Signal Process. 2003, 48–55 (2003)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  24. Zhu, X., Wu, X., Fan, J., et al.: Exploring video content structure for hierarchical summarization. Multimed. Syst. 10(2), 98–115 (2004)

    Article  Google Scholar 

  25. Chen, S.N.: Storyboard-based accurate automatic summary video editing system. Multimed. Tools Appl. (2016)

  26. FFMPEG. https://ffmpeg.org/. Accessed 29 May 2017

  27. Google Analytics Solutions. https://www.google.com/analytics/. Accessed 28 May 2017

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hyuncheol Kim or Jun Kyun Choi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, H., Yang, J., Kim, H. et al. Customized video service system design and implementation: from taste to image-based consuming method. Cluster Comput 22 (Suppl 1), 999–1009 (2019). https://doi.org/10.1007/s10586-017-1142-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1142-7

Keywords

Navigation