Abstract
Online video advertising (video-in-video) strategies are typically agnostic to the video content (ex.: advertising on YouTube) and the human viewer’s preferences. How to assess the emotional state and engagement of the viewer to place an advertisement? Where to insert an advertisement based on the content in an advertisement and a specific target video stream? Surely these are relevant questions that should be addressed by a good model for video advertisement placement. In this paper, we propose a novel framework to address two important aspects of (a) multi-modal affective analysis of video content and viewer behavior (b) a method for interactive personalized advertisement insertion for a single user. Our analysis and framework is backed by a systematic study of literature in marketing, consumer psychology and affective analysis of videos. Results from the user-study experiments demonstrate that the proposed method performs better than the state-of-the-art in video-in-video advertising.
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References
Hanjalic, A., Xu, L.-Q.: Affective video content representation and modeling. IEEE Transactions on Multimedia 7(1), 143–154 (2005)
Mellers, B.A., McGraw, A.P.: Anticipated emotions as guides to choice. Current Directions in Psychological Science 10(6), 210–214 (2001)
Bradley, M.M., Lang, P.J.: Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry 25, 49–59 (1994)
Roel Vertegaal, C.R., Shell, J.S., Mamuji, A.: Designing for augmented attention: Towards a framework for attentive user interfaces, vol. 22, pp. 771–789 (2006)
Forgas, J.P.: Toward Understanding the Role of Affect in Social Thinking and Behavior. Psychological Inquiry, 90–102 (2002)
Katti, H., Yadati, K., Kankanhalli, M., Tat-seng, C.: Affective VideoSummarization and Story Board Generation Using Pupillary Dilation and Eye-Gaze. In: 2011 IEEE International Symposium on Multimedia, pp. 319–326 (2011)
Russell, J.A.: A circumplex model of affect. Journal of Personality and Social Psychology 39(6), 1161–1178 (1980)
Broach, J., Carter, V., Page, J., Thomas, J., Wilson, R.D.: Television programming and its influence on viewers’ perceptions of commercials: The role of program arousal and pleasantness. Journal of Advertising 24(4), 45–54 (1995)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems 19, pp. 545–552. MIT Press (2007)
De Lemos, J., Sadeghnia, G.R., Ólafsdóttir, Í., Jensen, O.: Measuring emotions using eye tracking. In: Spink, A. (ed.) 6th International Conference on Methods and Techniques in Behavioral Research (2008)
Ekman, P.: Basic Emotions. In: Handbook of Cognition and Emotion. John Wiley and Sons (1996)
Hazlett, R.L., Hazlett, S.Y.: Emotional response to television commercials: Facial emg vs. self-report. Journal of Advertising Research 39(2), 7–23 (1999)
Mei, T., Hua, X.-S., Yang, L., Li, S.: Videosense: towards effective online video advertising. In: Proceedings of the 15th International Conference on Multimedia, MULTIMEDIA 2007, pp. 1075–1084 (2007)
Bolhuis, W.: Commercial breaks and ongoing emotions: Effects of program arousal and valence on emotions, memory and evaluation of commercials. Masters Thesis (2006)
Peng, W.-T., Chang, C.-H., Chu, W.-T., Huang, W.-J., Chou, C.-N., Chang, W.-Y., Hung, Y.-P.: A real-time user interest meter and its applications in home video summarizing. In: 2010 IEEE International Conference on Multimedia and Expo (ICME), pp. 849–854 (2010)
Rasheed, Z., Shah, M.: Scene detection in hollywood movies and tv shows. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-343–II-348 (2003)
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Yadati, K., Katti, H., Kankanhalli, M. (2013). Interactive Video Advertising: A Multimodal Affective Approach. In: Li, S., et al. Advances in Multimedia Modeling. MMM 2013. Lecture Notes in Computer Science, vol 7732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35725-1_10
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DOI: https://doi.org/10.1007/978-3-642-35725-1_10
Publisher Name: Springer, Berlin, Heidelberg
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