VideoSense: a contextual video advertising system

This demonstration presents a novel contextual advertising platform for online video service, called VideoSense. Unlike most current video-oriented sites that only display a video ad at the beginning or the end of a video, VideoSense aims to embed more contextually relevant ads at less intrusive positions within the video stream. Given an online video, VideoSense is able to detect a set of candidate ad insertion points based on content analysis, select a list of relevant candidate ads ranked according to textual relevance, and find the best match between insertion points and ads which maximizes the overall multimodal relevance. The effectiveness of VideoSense supporting contextually relevant and less intrusive advertising is validated by the user studies conducted on a variety of online video documents.

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

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

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

[4]  Meng Wang,et al.  MSRA-USTC-SJTU at TRECVID 2007: High-Level Feature Extraction and Search , 2007, TRECVID.

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

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

[7]  B. S. Manjunath,et al.  Automatic video annotation through search and mining , 2008, 2008 IEEE International Conference on Multimedia and Expo.

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

[9]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

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

[11]  John Boyd,et al.  The rise of intrusive online advertising and the response of user experience research at Yahoo! , 2004, CHI EA '04.

[12]  Mubarak Shah,et al.  Detection and representation of scenes in videos , 2005, IEEE Transactions on Multimedia.

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

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

[15]  Wei-Ying Ma,et al.  Probabilistic query expansion using query logs , 2002, WWW '02.

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

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

[18]  Lie Lu,et al.  Digital Object Identifier (DOI) 10.1007/s00530-002-0065-0 Multimedia Systems , 2003 .

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

[20]  Tao Mei,et al.  Modeling and Mining of Users' Capture Intention for Home Videos , 2007, IEEE Transactions on Multimedia.

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

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

[23]  Lie Lu,et al.  Optimization-based automated home video editing system , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

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

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

[26]  Jorge Sueiras,et al.  From TV to online advertising: recent experience from the Spanish media , 2007, ADKDD '07.

[27]  Changsheng Xu,et al.  Segmentation, categorization, and identification of commercial clips from TV streams using multimodal analysis , 2006, MM '06.

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

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

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

[31]  Tao Mei,et al.  Home Video Visual Quality Assessment With Spatiotemporal Factors , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[32]  Wei-Ying Ma,et al.  VIPS: a Vision-based Page Segmentation Algorithm , 2003 .

[33]  Yiming Yang,et al.  A re-examination of text categorization methods , 1999, SIGIR '99.