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作者:

Wang, Guanyao (Wang, Guanyao.) | Zhuo, Li (Zhuo, Li.) | Li, Jiafeng (Li, Jiafeng.) | Ren, Dongyue (Ren, Dongyue.) | Zhang, Jing (Zhang, Jing.)

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EI Scopus SCIE

摘要:

With the rapid development of Internet, the views of the online video have increased dramatically. Meanwhile, the corresponding online video advertising market showed a momentum of rapid and sustained development. In order to attract more potential purchasers and reduce the interference on the ordinary video browsers, many researchers and enterprises have conducted the research of video online advertising. At present, the insertion methods of most video advertising are always position-fixed, mandatory timing, quantitative, and the relevance of advertisement content and the video content is usually ignored. These methods will inevitably reduce the advertising effect because of browsers' dissatisfaction and resistance. In order to overcome the shortages of the existing methods of video advertisement insertion, this paper proposed an effective content-targeted method for online video advertising. The insertion of advertisement is determined by comparing the content of the videos and the advertisements. At the same time, the characteristics of the scene switching in the video are taken into account to select the appropriate position of the advertisement insertion. Experimental results show that our method can provide a better user experience than existing methods, and its attractiveness and comfortableness is greatly improved.

关键词:

Similarity measurement Content-targeted Advertising insertion Key frame extraction Scene boundary detection

作者机构:

  • [ 1 ] [Wang, Guanyao]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 2 ] [Zhuo, Li]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 3 ] [Li, Jiafeng]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 4 ] [Ren, Dongyue]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 5 ] [Zhang, Jing]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 6 ] [Zhuo, Li]Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing, Peoples R China

通讯作者信息:

  • [Zhuo, Li]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China

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来源 :

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION

ISSN: 1047-3203

年份: 2018

卷: 50

页码: 40-48

2 . 6 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:161

被引次数:

WoS核心集被引频次: 18

SCOPUS被引频次: 21

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

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