On Detection of Advertising Images
ABSTRACT Online advertising has enjoyed exponential growth in recent years, and many advertisements appear in the form of images. Although it makes considerable profit, these advertisements tend to disturb the Internet surfing of normal users. Moreover, they always bring extra burden in indexing to commercial image search engines. Therefore, it is necessary to automatically detect those advertising images on the Web. In this paper, a classification based approach is proposed for advertising image detection, in which comprehensive features are exploited and effectively combined. Those features include visual content, link, text and visual layout in hosting Web pages. Promising experimental results are obtained on images collected from about 480 Web sites.
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Conference Proceeding: Image classification for mobile web browsing.[show abstract] [hide abstract]
ABSTRACT: It is difficult for users of mobile devices such as cellular phones equipped with a small screen and a poor input in- terface to browse Web pages designed for desktop PCs with large displays. Many studies and commercial products have tried to solve this problem. Web pages include images that have various roles such as site menus, line headers for item- ization, and page titles. However, most studies of mobile Web browsing haven't paid much attention to the roles of Web images. In this paper, we define eleven Web image cat- egories according to their roles and use these categories for proper Web image handling. We manually categorized 3,901 Web images collected from forty Web sites and extracted image features of each category according to the classifica- tion. By making use of the extracted features, we devised an automatic Web image classification method. Furthermore, we evaluated the automatic classification of real Web pages and achieved up to 83.1% classification accuracy. We also implemented an automatic Web page scrolling system as an application of our automatic image classification method.Proceedings of the 15th international conference on World Wide Web, WWW 2006, Edinburgh, Scotland, UK, May 23-26, 2006; 01/2006
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ABSTRACT: AdEater is a fully implemented browsing assistant that automatically removes advertisement images from Internet pages. Unlike related systems that rely on hand-crafted rules, AdEater takes an inductive learning approach, automatically generating rules from training examples. Our experiments demonstrate that our approach is practical: the off-line training phase takes less than six minutes; on-line classification takes about 70 msec; and classification accuracy exceeds 97% given a modest set of training data. 1 Introduction Many Internet sites draw income from third-party advertisements, usually in the form of images sprinkled throughout the site's pages. If judged to be interesting or relevant, users can click on these so-called "banner advertisements", jumping to the advertiser's own site. Some users prefer not to view such advertisements. Images tend to dominate a page's total download time, so users connecting through slow links find that advertisements substantially impede their...03/1999;
Conference Proceeding: Automatic removal of advertising from web-page display.[show abstract] [hide abstract]