Cited By
View all- Nguyen TZhao QYan S(2018)Attentive SystemsInternational Journal of Computer Vision10.1007/s11263-017-1042-6126:1(86-110)Online publication date: 1-Jan-2018
ImageSense
Pages 1027 - 1028
Abstract
This 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 contents of the Web page, ImageSense aims to embed more contextually relevant ads at less intrusive positions within each suitable image. Given a Web page containing images, ImageSense is able to decompose the page into a set of semantic blocks, select the suitable images from these blocks for advertising, rank the ads according to the relevance derived from surrounding text and visual similarity, and insert the relevant ads into the nonintrusive areas within the selected images. ImageSense represents one of the first attempts towards contextual image advertising which enables both the publishers and advertisers deliver more effective ads carried through image contents.
References
[1]
AdSense. http://www.google.com/adsense/.
[2]
R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 1999.
[3]
D. Cai, S. Yu, J.-R. Wen, and W.-Y. Ma. VIPS: a vision-based page segmentation algorithm. In Microsoft Technical Report, 2003.
[4]
Live Image Search. http://www.live.com/~&scope=images.
[5]
Y.-F. Ma and H.-J. Zhang. Contrast-based image attention analysis by using fuzzy growing. In ACM Multimedia, 2003.
[6]
T. Mei, X.-S. Hua, L. Yang, and S. Li. VideoSense: Towards effective online video advertising. In ACM Multimedia, 2007.
[7]
Yahoo! Image. http://images.search.yahoo.com/images.
Index Terms
- ImageSense
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Information
Published In
October 2008
1206 pages
ISBN:9781605583037
DOI:10.1145/1459359
- General Chairs:
- Abdulmotaleb EL Saddik,
- Son Vuong,
- Program Chairs:
- Carsten Griwodz,
- Alberto Del Bimbo,
- K. Selcuk Candan,
- Alejandro Jaimes
Copyright © 2008 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 October 2008
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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%
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Cited By
View all- Nguyen TZhao QYan S(2018)Attentive SystemsInternational Journal of Computer Vision10.1007/s11263-017-1042-6126:1(86-110)Online publication date: 1-Jan-2018
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