ImageSense: Towards contextual image advertising

The daunting volumes of community-contributed media contents on the Internet have become one of the primary sources for online advertising. However, conventional advertising treats image and video advertising as general text advertising by displaying relevant ads based on the contents of the Web page, without considering the inherent characteristics of visual contents. This article presents a 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, supports scalable advertising of, from root to node, Web sites, pages, and images. In ImageSense, the 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 salience, as well as face and text detection, to minimize intrusiveness to the user. We evaluate ImageSense on a large-scale real-world images and Web pages, and demonstrate the effectiveness of ImageSense for online image advertising.

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

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

[3]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

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

[5]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[6]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

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

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

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

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

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

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

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

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

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

[16]  Mor Naaman,et al.  How flickr helps us make sense of the world: context and content in community-contributed media collections , 2007, ACM Multimedia.

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

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

[19]  Wei-Ying Ma,et al.  Clustering and searching WWW images using link and page layout analysis , 2007, TOMCCAP.

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

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

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

[23]  Susanne Boll,et al.  'Oh Web Image, Where Art Thou?' , 2008, MMM.

[24]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

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

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

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

[28]  Newton Lee,et al.  ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP) , 2007, CIE.

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

[30]  Svetha Venkatesh,et al.  Extraction of social context and application to personal multimedia exploration , 2006, MM '06.

[31]  Tao Mei,et al.  VideoSense: a contextual video advertising system , 2007, ACM Multimedia.

[32]  Tao Mei,et al.  Contextual in-image advertising , 2008, ACM Multimedia.

[33]  P. Chatterjee,et al.  Modeling the Clickstream: Implications for Web-Based Advertising Efforts , 2003 .

[34]  William Brown,et al.  Psychology and life , 1934 .

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

[36]  Aranyak Mehta,et al.  AdWords and Generalized On-line Matching , 2005, FOCS.

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

[38]  Wei-Ying Ma,et al.  Delivering online advertisements inside images , 2008, ACM Multimedia.

[39]  Lora Aroyo,et al.  Personalized ambient media experience: move.me case study , 2007, IUI '07.