ABSTRACT
The community-contributed media contents over the Internet have become one of the primary sources for online advertising. However, conventional ad-networks such as Google AdSense treat image and video advertising as general text advertising without considering the inherent characteristics of visual contents. In this work, we propose an innovative contextual advertising system driven by images, which automatically associates relevant ads with an image rather than the entire text in a Web page and seamlessly inserts the ads in the nonintrusive areas within each individual image. The proposed system, called ImageSense, represents the first attempt towards contextual in-image advertising. The relevant ads are selected based on not only textual relevance but also visual similarity so that the ads yield contextual relevance to both the text in the Web page and the image content. The ad insertion positions are detected based on image saliency to minimize intrusiveness to the user. We evaluate ImageSense on three photo-sharing sites with around one million images and 100 Web pages collected from several major sites, and demonstrate the effectiveness of ImageSense.
- AdSense. http://www.google.com/adsense/.Google Scholar
- R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 1999. Google ScholarDigital Library
- S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004. Google ScholarDigital Library
- BritePic. http://www.britepic.com/.Google Scholar
- A. Broder, M. Fontoura, V. Josifovski, and L. Riedel. A semantic approach to contextual advertising. In Proceedings of SIGIR, 2007. Google ScholarDigital Library
- D. Cai, S. Yu, J.-R. Wen, and W.-Y. Ma. VIPS: a vision-based page segmentation algorithm. In Microsoft Technical Report (MSR-TR-2003-79), 2003.Google Scholar
- CNN. http://www.cnn.com/.Google Scholar
- Flickr. http://www.flickr.com/.Google Scholar
- R. J. Gerrig and P. G. Zimbardo. Psychology and Life (16 Edition). Allyn & Bacon, 2001.Google Scholar
- X. He, D. Cai, J.-R. Wen, W.-Y. Ma, and H.-J. Zhang. Clustering and searching www images using link and page layout analysis. ACM Transactions on Multimedia Computing, Communications, and Applications, 3(2), 2007. Google ScholarDigital Library
- J. Hu, H.-J. Zeng, H. Li, C. Niu, and Z. Chen. Demographic prediction based on user's browsing behavior. In Proceedings of WWW, 2007. Google ScholarDigital Library
- A. Joshi and R. Motwani. Keyword generation for search engine advertising. In Proceedings of the Workshops of IEEE International Conference on Data Mining, 2006. Google ScholarDigital Library
- L. Kennedy, M. Naaman, S. Ahern, R. Nair, and T. Rattenbury. How Flickr helps us make sense of the world: Context and content in community-contributed media collections. In Proceedings of ACM Multimedia, Augsburg, Germany, 2007. Google ScholarDigital Library
- A. Lacerda, M. Cristo, M. A. Goncalves, and phet al. Learning to advertise. In Proceedings of SIGIR, 2006. Google ScholarDigital Library
- H. Li, S. M. Edwards, and J.-H. Lee. Measuring the intrusiveness of advertisements: scale development and validation. Journal of Advertising, 31(2):37--47, 2002.Google ScholarCross Ref
- H. Li, D. Zhang, J. Hu, H.-J. Zeng, and Z. Chen. Finding keyword from online broadcasting content for targeted advertising. In International Workshop on Data Mining and Audience Intelligence for Advertising, 2007. Google ScholarDigital Library
- Live Image Search. http://www.live.com/~&scope=images.Google Scholar
- Y.-F. Ma and H.-J. Zhang. Contrast-based image attention analysis by using fuzzy growing. In Proceedings of ACM Multimedia, pages 374--381, Nov 2003. Google ScholarDigital Library
- S. Mccoy, A. Everard, P. Polak, and D. F. Galletta. The effects of online advertising. Communications of The ACM, 50(3):84--88, 2007. Google ScholarDigital Library
- A. Mehta, A. Saberi, U. Vazirani, and V. Vazirani. Adwords and generalized on-line matching. Journal of the ACM, 54(5), Oct. 2007. Google ScholarDigital Library
- T. Mei, X.-S. Hua, W. Lai, L. Yang, and et al. MSRA-USTC-SJTU at TRECVID 2007: High-level feature extraction and search. In TREC Video Retrieval Evaluation Online Proceedings, 2007.Google Scholar
- T. Mei, X.-S. Hua, L. Yang, and S. Li. VideoSense: Towards effective online video advertising. In Proceedings of ACM Multimedia, pages 1075--1084, Augsburg, Germany, 2007. Google ScholarDigital Library
- V. Murdock, M. Ciaramita, and V. Plachouras. A noisy channel approach to contextual advertising. In International Workshop on Data Mining and Audience Intelligence for Advertising, 2007. Google ScholarDigital Library
- MySpace. http://www.myspace.com/.Google Scholar
- photoSIG. http://www.photosig.com/.Google Scholar
- B. Ribeiro-Neto, M. Cristo, P. B. Golgher, and E. S. Moura. Impedance coupling in content-targeted advertising. In Proceedings of SIGIR, 2005. Google ScholarDigital Library
- M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: Estimating click-through rate for new ads. In Proceedings of WWW, 2007. Google ScholarDigital Library
- S. E. Robertson, S. Walker, and M. Hancock-Beaulieu. Okapi at TREC-7. In the Seventh Text REtrieval Conference, Gaithersburg, USA, Nov. 1998.Google Scholar
- D. Shen, J.-T. Sun, Q. Yang, and Z. Chen. Building bridges for web query classification. In Proceedings of SIGIR, 2006. Google ScholarDigital Library
- S. H. Srinivasan, N. Sawant, and S. Wadhwa. vADeo - video advertising system. In Proceedings of ACM Multimedia, 2007. Google ScholarDigital Library
- TRECVID. http://www-nlpir.nist.gov/projects/trecvid/.Google Scholar
- Yahoo! Image. http://images.search.yahoo.com/images.Google Scholar
- B. Yang, T. Mei, X.-S. Hua, L. Yang, S.-Q. Yang, and M. Li. Online video recommendation based on multimodal fusion and relevance feedback. In Proceedings of CIVR, 2007. Google ScholarDigital Library
- Y. Yang and X. Liu. A re-examination of text categorization methods. In Proceedings of SIGIR, 1999. Google ScholarDigital Library
- W.-T. Yih, J. Goodman, and V. R. Carvalho. Finding advertising keywords on web pages. In Proceedings of WWW, 2006. Google ScholarDigital Library
Index Terms
- Contextual in-image advertising
Recommendations
ImageSense
MM '08: Proceedings of the 16th ACM international conference on MultimediaThis demonstration presents an innovative contextual advertising platform for online image service, called ImageSense. Unlike most current ad-networks which treat image advertising as general text advertising by displaying relevant ads based on the ...
Scientific challenges in contextual advertising
RSKT'10: Proceedings of the 5th international conference on Rough set and knowledge technologyOnline advertising has been fueling the rapid growth of the Web that offers a plethora of free web services, ranging from search, email, news, sports, finance, and video, to various social network services. Such free services have accelerated the shift ...
Is Combining Contextual and Behavioral Targeting Strategies Effective in Online Advertising?
Online targeting has been increasingly used to deliver ads to consumers. But discovering how to target the most valuable web visitors and generate a high response rate is still a challenge for advertising intermediaries and advertisers. The purpose of ...
Comments