Contextual In-image Advertising

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.