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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ABSTRACT: The advent of media-sharing sites has led to the unprecedented Internet delivery of community-contributed media like images and videos. Those visual contents have become the primary sources for online advertising. Conventional advertising treats multimedia advertising as general text advertising by displaying advertisements either relevant to the queries or the Web pages, without considering the potential advantages which could be brought by media contents. In this paper, we summarize the trend of Internet multimedia advertising and conduct a broad survey on the methodologies for advertising which are driven by the rich contents of images and videos. We discuss three key problems in a generic multimedia advertising framework. These problems are: contextual relevance that determines the selection of relevant advertisements, contextual intrusiveness which is the key to detect appropriate ad insertion positions within an image or video, and insertion optimization that achieves the best association between the advertisements and insertion positions so that the effectiveness of advertising can be maximized in terms of both contextual relevance and contextual intrusiveness. We show recently developed MediaSense which consists of image, video, and game advertising as an exemplary application of contextual multimedia advertising. In the MediaSense, the most contextually relevant ads are embedded at the most appropriate positions within images or videos. To this end, techniques in computer vision, multimedia retrieval, and computer human interaction are leveraged. We also envision that the next trend of multimedia advertising would be game-like advertising which is more impressionative and thus can promote advertising in an interactive, as well as more compelling and effective way. We conclude this survey with a brief outlook on open research directions.Proceedings of the IEEE 09/2010; DOI:10.1109/JPROC.2009.2039841 · 5.47 Impact Factor
Conference Paper: Advertisement detection in digitized press images.[Show abstract] [Hide abstract]
ABSTRACT: This paper presents the first method for detecting advertisements in digitized press. The system aims at locating and recognizing ads. A color segmentation approach which is robust against digitization noise is introduced. The color separation output is used to carry out layout segmentation in document pages and to compute visual features. Block classification results, given with a variety of magazine and newspaper pages, are presented and discussed.Proceedings of the 2011 IEEE International Conference on Multimedia and Expo, ICME 2011, 11-15 July, 2011, Barcelona, Catalonia, Spain; 01/2011
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ABSTRACT: Web advertising has become a major industry and a large part of this market consists of contextual ads. Although it has made a great impact on earnings of many publishers' websites, these advertisements tend to disturb the internet surfing of normal users and to consume a lot of valuable bandwidth. Moreover, they always bring extra burden in indexing to commercial search engines as they mix up with the main content of the hosting web pages. Therefore, it is necessary to automatically detect those contextual ads on the web. In this paper, a classification based approach is proposed for contextual ads detection. Those features include text, link, layout and style in hosting web pages. Furthermore, neural network is used to identify the parameters that contribute the most in detecting contextual ads from non-contextual ads. Promising experimental results are obtained on ATOM textual snippets collected from 219 web sites.