Advertising object in web videos

  • Authors:

  • Richang Hong;Linxie Tang;Jun Hu;Guangda Li;Jian-Guo Jiang

  • Affiliations:

  • School of Computer and Information, Hefei University of Technology, Hefei 230009, China;Department of EEIS, University of Science and Technology of China, Hefei 230026, China;College of Computer Science, Zhejiang University, Hangzhou 310027, China;School of Computing, National University of Singapore, 117417 Singapore, Singapore;School of Computer and Information, Hefei University of Technology, Hefei 230009, China

  • Venue:
  • Neurocomputing
  • Year:
  • 2013

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Abstract

We have witnessed the booming of contextual video advertising in recent years. However, those advertisement systems solely take the metadata into account, such as titles, descriptions and tags. This kind of text-based contextual advertising reveals a number of shortcomings in ads insertion and ads association. In this paper, we present a novel video advertising system called VideoAder. The system leverages the well organized media information from the video corpus for embedding visual content relevant ads into a set of precisely located insertion position. Given a product, we utilize content-based object retrieval technique to identify the relevant ads and their potential embedding positions in the video stream. Then we formulate the ads association as an optimization problem to maximize the total revenue for the system. Specifically, the ''Single-Merge'' and ''Merge'' methods are proposed to tackle the complex query in visual representation. Typical Feature Intensity (TFI) is used to train a classifier to automatically decide which method is more representive. Experimental results demonstrated the accuracy and feasibility of the system.