ImageSense

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: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 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: Yu-Fei Ma , Hong-Jiang Zhang, Contrast-based image attention analysis by using fuzzy growing, Proceedings of the eleventh ACM international conference on Multimedia, November 02-08, 2003, Berkeley, CA, USA [doi>10.1145/957013.957094]
• 6: Tao Mei , Xian-Sheng Hua , Linjun Yang , Shipeng Li, VideoSense: towards effective online video advertising, Proceedings of the 15th international conference on Multimedia, September 25-29, 2007, Augsburg, Germany [doi>10.1145/1291233.1291467]
• 7: Yahoo! Image. http://images.search.yahoo.com/images.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.5 On-line Information Services
          Subjects: Web-based services

General Terms:
Algorithms, Experimentation, Human Factors

Keywords:
image advertising, image saliency, web page segmentation