VideoAder: A video advertising system based on intelligent analysis of visual content

Jun Hu, Guangda Li, Zhen Lu, Jun Xiao, Richang Hong

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Recent years have witnessed the prevalence of context based video advertisement. However, those advertisement systems solely take the metadata into account, such as titles, descriptions and tags. In this paper, we present a novel video advertising system called VideoAder. The system leverages the rich information from the video corpus for embedding visual content relevant ads. Given a product, we utilize content-based object retrieval technique to identify the relevant ads and their potential embedding positions in the video stream. Specifically, the "Single-Merge" and "Merge" methods are proposed to tackle the complex query. Typical Feature Intensity (TFI) is used to train a classifier to automatically deciding which method is better in one situation. Experimental results demonstrated the feasibility of the system.

Original languageEnglish
Title of host publicationICIMCS 2011 - 3rd International Conference on Internet Multimedia Computing and Service, Proceedings
Pages30-33
Number of pages4
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event3rd International Conference on Internet Multimedia Computing and Service, ICIMCS 2011 - Chengdu, China
Duration: 5 Aug 20117 Aug 2011

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Internet Multimedia Computing and Service, ICIMCS 2011
Country/TerritoryChina
CityChengdu
Period5/08/117/08/11

Keywords

  • product
  • video advertising
  • visual relevance

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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