Contextual In-Stream Video Advertising

With Internet delivery of video content surging to an unprecedented level, online video advertising is becoming increasingly pervasive. In this chapter, we present a new advertising paradigm for online video, called contextual in-stream video advertising, which automatically associates the most relevant video ads with online videos and seamlessly inserts the ads at the most appropriate spatiotemporal positions within each individual video. Different from most current video-oriented sites that only display the ads at the predefined locations in a video, this advertising paradigm aims to embed more contextually relevant ads at less intrusive positions within the video stream nonlinearly. We introduce the following key techniques in this paradigm: video processing for ad location detection, text analysis for ad selection, and optimization for ad insertion. We also describe two recently developed systems as showcases, i.e., VideoSense and AdOn which support in-stream inline and overlay advertising, respectively.

[1]  Tao Mei,et al.  VideoSense: towards effective online video advertising , 2007, ACM Multimedia.

[2]  Winston H. Hsu,et al.  AdImage: video advertising by image matching and ad scheduling optimization , 2008, SIGIR '08.

[3]  Aranyak Mehta,et al.  AdWords and generalized on-line matching , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).

[4]  Tao Mei,et al.  Contextual Internet Multimedia Advertising , 2010, Proceedings of the IEEE.

[5]  Jian Hu,et al.  Finding keyword from online broadcasting content for targeted advertising , 2007, ADKDD '07.

[6]  Li Zhao,et al.  Video shot grouping using best-first model merging , 2001, IS&T/SPIE Electronic Imaging.

[7]  Petros Maragos,et al.  Video event detection and summarization using audio, visual and text saliency , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Joshua Goodman,et al.  Finding advertising keywords on web pages , 2006, WWW '06.

[9]  Tammi S. Feltham,et al.  Program involvement and ad/program consistency as moderators of program context effects , 1994 .

[10]  HongJiang Zhang,et al.  Contrast-based image attention analysis by using fuzzy growing , 2003, MULTIMEDIA '03.

[11]  Hua Li,et al.  Demographic prediction based on user's browsing behavior , 2007, WWW '07.

[12]  Srinivasan H. Sengamedu,et al.  vADeo: video advertising system , 2007, ACM Multimedia.

[13]  Robin Cohen,et al.  An Approach for Delivering Personalized Advertisements in Interactive TV Customized to Both Users and Advertisers , 2007 .

[14]  Amit Thawani,et al.  Context Aware Personalized Ad Insertion in an Interactive TV Environment , 2004 .

[15]  Rajeev Motwani,et al.  Keyword Generation for Search Engine Advertising , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[16]  Andrea Everard,et al.  The effects of online advertising , 2007, Commun. ACM.

[17]  Ja-Ling Wu,et al.  ViSA: virtual spotlighted advertising , 2008, ACM Multimedia.

[18]  Berthier A. Ribeiro-Neto,et al.  Impedance coupling in content-targeted advertising , 2005, SIGIR '05.

[19]  Changsheng Xu,et al.  A generic virtual content insertion system based on visual attention analysis , 2008, ACM Multimedia.

[20]  Atreyi Kankanhalli,et al.  Automatic partitioning of full-motion video , 1993, Multimedia Systems.

[21]  HongJiang Zhang,et al.  Text Area Detection from Video Frames , 2001, IEEE Pacific Rim Conference on Multimedia.

[22]  Lie Lu,et al.  A generic framework of user attention model and its application in video summarization , 2005, IEEE Trans. Multim..

[23]  Tao Mei,et al.  AdOn: toward contextual overlay in-video advertising , 2010, Multimedia Systems.

[24]  Tao Mei,et al.  Automatic Video Genre Categorization using Hierarchical SVM , 2006, 2006 International Conference on Image Processing.

[25]  Changsheng Xu,et al.  Robust goal-mouth detection for virtual content insertion , 2003, MULTIMEDIA '03.

[26]  Hairong Li,et al.  Measuring the Intrusiveness of Advertisements: Scale Development and Validation , 2002 .

[27]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[28]  Harry Shum,et al.  Statistical Learning of Multi-view Face Detection , 2002, ECCV.

[29]  Ying Li,et al.  Detecting online commercial intention (OCI) , 2006, WWW '06.

[30]  Andrei Z. Broder,et al.  A semantic approach to contextual advertising , 2007, SIGIR.

[31]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[32]  Tao Mei,et al.  Online video recommendation based on multimodal fusion and relevance feedback , 2007, CIVR '07.

[33]  Tao Mei,et al.  AdOn: an intelligent overlay video advertising system , 2009, SIGIR.

[34]  Vassilis Plachouras,et al.  A noisy-channel approach to contextual advertising , 2007, ADKDD '07.

[35]  Keith S. Coulter.  The Effects of Affective Responses to Media Context on Advertising Evaluations , 1998 .

[36]  Matthew Richardson,et al.  Predicting clicks: estimating the click-through rate for new ads , 2007, WWW '07.

[37]  Shiliang Zhang,et al.  Affective MTV analysis based on arousal and valence features , 2008, 2008 IEEE International Conference on Multimedia and Expo.

[38]  Changsheng Xu,et al.  Real time advertisement insertion in baseball video based on advertisement effect , 2005, MULTIMEDIA '05.

[39]  Qiang Yang,et al.  Building bridges for web query classification , 2006, SIGIR.

[40]  Weiguo Fan,et al.  Learning to advertise , 2006, SIGIR.

[41]  Tao Mei,et al.  VideoSense: A Contextual In-Video Advertising System , 2009, IEEE Transactions on Circuits and Systems for Video Technology.