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| Content Provider | ACM Digital Library |
|---|---|
| Author | Hua, Xian-Sheng Mei, Tao Li, Shipeng Li, Lusong |
| Copyright Year | 2012 |
| Abstract | 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 $\textit{ImageSense},$ 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. |
| Starting Page | 1 |
| Ending Page | 18 |
| Page Count | 18 |
| File Format | |
| ISSN | 15516857 |
| e-ISSN | 15516865 |
| DOI | 10.1145/2071396.2071402 |
| Volume Number | 8 |
| Issue Number | 1 |
| Journal | ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-02-03 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Hardware and Architecture Computer Networks and Communications |
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