Skip to main content
SpringerLink
Log in

AdOn: toward contextual overlay in-video advertising

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

This paper presents a contextual video advertising system, called AdOn, which supports intelligent overlay in-video advertising. Unlike most current ad-networks such as Youtube that overlay the ads at fixed locations in the videos (e.g., on the bottom fifth of videos 15 s in), AdOn is able to automatically detect a set of spatio-temporal non-intrusive locations and associate the contextually relevant ads with these locations. The overlay ad locations are obtained on the basis of video structuring, face and text detection, as well as visual saliency analysis, so that the intrusiveness to the users can be minimized. The ads are selected according to content-based multimodal relevance so that the relevance can be maximized. AdOn represents one of the first attempts towards contextual overlay video advertising by leveraging information retrieval and multimedia content analysis techniques. The experiments conducted on a video database with more than 100 video programs and 7,000 ad products indicated that AdOn is superior to existing advertising approaches in terms of ad relevance and user experience.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Notes

  1. Please note that Revver subscribes overlay ad service from Google’s AdSense [1].

  2. There is usually an accompany ad around the video at the same time.

  3. Each overlay ad location is a spatio-temporal location in a shot with a predefined duration, e.g., 10 or 15 s. We neglect the shots with the duration less than that of ad.

  4. We used Scansoft which has been integrated into Microsoft Office.

  5. Note that more than half of the 100 videos are with 1–3 min.

References

  1. AdSense. http://www.google.com/adsense/

  2. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley, Reading, MA (1999)

    Google Scholar 

  3. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)

    MATH  Google Scholar 

  4. Chang, C.-H., Hsieh, K.-Y., Chung, M.-C., Wu, J.-L.: ViSA: virtual spotlighted advertising. In: Proceedings of ACM Multimedia, pp. 837–840 (2008)

  5. Chen, X., Zhang, H.-J.: Text area detection from video frames. In: Proceedings of the IEEE Pacific Rim Conference on Multimedia, pp. 222–228 (2001)

  6. Coulter, K.S.: The effects of affective response to media context on advertising evaluations. J. Advert. XXVII(4), 41–51 (1998)

    Google Scholar 

  7. Evangelopoulos, G., Zlatintsi, A., Skoumas, G., Rapantzikos, K., Potamianos, A., Maragos, P., Avrithis, Y.: Video event detection and summarization using audio, visual and text saliency. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (2009)

  8. Feltham, T.S., Arnold, S.J.: Program involvement and ad/program consistency as moderators of program context effects. J. Consum. Psychol. 3(1), 51–77 (1994)

    Article  Google Scholar 

  9. Guo, J., Mei, T., Liu, F., Hua, X.-S.: AdOn: an intelligent overlay video advertising system. In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 628–629 (2009)

  10. Hua, X.-S., Mei, T., Li, S.: When multimedia advertising meets the new internet era. In: Proceedings of IEEE International Workshop on Multimedia Signal Processing, pp. 1–5 (2008)

  11. Kastidou, G., Cohen, R.: An approach for delivering personalized ads in interactive TV customized to both users and advertisers. In: Proceedings of European Conference on Interactive Television (2006)

  12. Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H.-J., Shum, H.: Statistical learning of multi-view face detection. In: Proceedings of European Conference on Computer Vision, Copenhagen, Denmark, pp. 67–81 (2002)

  13. Li, Y., Wan, K., Yan, X., Xu, C.: Advertisement insertion in baseball video based on advertisement effect. In: Proceedings of ACM Multimedia, pp. 343–346 (2005)

  14. Liao, W.-S., Chen, K.-T., Hsu, W.H.: AdImage: video advertising by image matching and ad scheduling optimization. In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 767–768 (2008)

  15. Liu, H., Jiang, S., Huang, Q., Xu, C.: A generic virtual content insertion system based on visual attention analysis. In: Proceeding of the ACM International Conference on Multimedia, pp. 379–388 (2008)

  16. LiveRail. Q4 2008 state of the industry.

  17. Ma, Y.-F., Hua, X.-S., Lu, L., Zhang, H.-J.: A generic framework of user attention model and its application in video summarization. IEEE Trans. Multimedia 7(5), 907–919 (2005)

    Article  Google Scholar 

  18. Ma, Y.-F., Zhang, H.-J.: Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of ACM Multimedia, pp. 374–381 (2003)

  19. mad.co.uk. Only 9% of viewers find overlay video ads intrusive. http://www.mad.co.uk/BreakingNews/BreakingNews/Articles/e2b9d199d9794e9195e6801c833dc97c/Only-9-of-viewers-find-overlay-video-ads-intrusive,-finds-ITV.html

  20. Mccoy, S., Everard, A., Polak, P., Galletta, D.F.: The effects of online advertising. Commun. ACM 50(3), 84–88 (2007)

    Article  Google Scholar 

  21. MediaPost. Google: This is your brain on advertising. http://www.mediapost.com/publications/?fa=Articles.showArticle&art_aid=93319

  22. Mei, T., Hua, X.-S., Li, S.: Contextual in-image advertising. In: Proceedings of ACM Multimedia, Vanconver, Canada, pp. 439–448 (2008)

  23. Mei, T., Hua, X.-S., Yang, L., Li, S.: VideoSense: towards effective online video advertising. In: Proceedings of ACM Multimedia, Augsburg, Germany, pp. 1075–1084 (2007)

  24. ReelSEO. Pre-roll vs. overlay—both video ad formats are effective. http://www.reelseo.com/video-ad-formats-research/

  25. Revver. http://one.revver.com/revver

  26. Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. Circuits Video Technol. 8(5), 644–655 (1998)

    Article  Google Scholar 

  27. Srinivasan, S.H., Sawant, N., Wadhwa, S.: vADeo: video advertising system. In: Proceedings of ACM Multimedia, pp. 455–456 (2007)

  28. Thawani, A., Gopalan, S., Sridhar, V.: Context aware personalized ad insertion in an interactive TV environment. In: Proceedings of Workshop on Personalization in Future TV (2004)

  29. Videoegg. http://www.videoegg.com/

  30. Wan, K., Yan, X., Yu, X., Xu, C.: Robust goal-mouth detection for virtual content insertion. In: Proceedings of ACM Multimedia, pp. 468–469 (2003)

  31. Yang, B., Mei, T., Hua, X.-S., Yang, L., Yang, S.-Q., Li, M.: Online video recommendation based on multimodal fusion and relevance feedback. In: Proceedings of ACM International Conference on Image and Video Retrieval (2007)

  32. YouTube. http://www.youtube.com/

  33. Yuan, X., Lai, W., Mei, T., Hua, X.-S., Wu, X.-Q.: Automatic video genre categorization using hierarchical svm. In: Proceedings of IEEE International Conference on Image Processing, Atlanta, USA, (2006)

  34. Zhang, H.-J., Kankanhalli, A., Smoliar, S.W.: Automatic partitioning of full-motion video. Multimedia Syst. 1(1), 10–28 (1993)

    Article  Google Scholar 

  35. Zhang, S., Tian, Q., Jiang, S., Huang, Q., Gao, W.: Affective mtv analysis based on arousal and valence features. In: Proceedings of ICME, pp. 1369–1372 (2008)

Download references

Author information

Authors and Affiliations

  1. Microsoft Research Asia, Beijing, 100190, China

    Tao Mei & Xian-Sheng Hua

  2. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China

    Jinlian Guo & Falin Liu

Authors
  1. Tao Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinlian Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xian-Sheng Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Falin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tao Mei.

Additional information

Part of this work was published at SIGIR 2009 as a poster [9].

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mei, T., Guo, J., Hua, XS. et al. AdOn: toward contextual overlay in-video advertising. Multimedia Systems 16, 335–344 (2010). https://doi.org/10.1007/s00530-010-0195-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-010-0195-8

Keywords

Search

Navigation