Advertise gently - in-image advertising with low intrusiveness

  • Authors:

  • Huiying Liu;Xuekan Qiu;Qingming Huang;Shuqiang Jiang;Changsheng Xu

  • Affiliations:

  • Key Lab of Int. Inf. Proc., Chinese Academy of Sci., China and Inst. of Computing Techn., Beijing, China and Grad. Univ. of Chinese Academy of Sci., Beijing, China and China-Singapore Inst. of Dig ...;Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, China and Graduate University of Chinese Academy of Sciences, Beijing, China;Key Lab of Int. Inf. Proc., Chinese Academy of Sci., China and Inst. of Computing Techn., Beijing, China and Grad. Univ. of Chinese Academy of Sci., Beijing, China and China-Singapore Inst. of Dig ...;Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, China and Institute of Computing Technology, Beijing, China and;China-Singapore Institute of Digital Media, Heng Mui Keng Terrace, Singapore and National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China

  • Venue:
  • ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
  • Year:
  • 2009

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Abstract

The new trend of online advertisement is in-image advertising, which is facing the risk of being intrusive. Several works have been done to reduce the intrusiveness. However, intrusiveness is a subjective concept and is difficult to be measured objectively. In this paper, by considering the fact that gentle advertising will not disturb audiences' attention too much but the intrusive ones will, we investigate the relationship between intrusiveness and audience attention. By experiment, we find that two aspects of attention will affect intrusiveness. Firstly, if the inserted advertisement covers the Region of Interest (ROI), it is truly very intrusive. Secondly, if the advertisement distracts audience attention from the original attending point, it is also very intrusive. We measure intrusiveness from the above two aspects. Using this measurement, we insert advertisements into online image collections gently. Given a pair of an image and an advertisement, we detect the suitable place, using attention analysis and visual consistency, to reduce intrusiveness. Given an image set and an advertisement set, we minimize the intrusiveness by searching for an optimal match. Experimental results verify the effectiveness of the proposed measurement of intrusiveness and of the advertising approach.