• DocumentCode
    261413
  • Title

    An improved logo detection method with learning-based verification for video classification

  • Author

    Hyo-Young Kim ; Mun-Cheon Kang ; Sung-Ho Chae ; Dae-Hwan Kim ; Sung-Jea Ko

  • Author_Institution
    Dept. of Electron. Eng., Korea Univ., Seoul, South Korea
  • fYear
    2014
  • fDate
    7-10 Sept. 2014
  • Firstpage
    192
  • Lastpage
    193
  • Abstract
    With the growth of cloud services, concerns have been raised regarding illegal sharing of the commercial video. To prevent the illegal sharing automatically, the method for classifying video as `commercial´ or `noncommercial´ is essentially required. Since most commercial video has a logo as a visible watermark, automatic logo detection can be an efficient method for the video classification. In this paper, we present an improved logo detection method which correctly detects the logo in any types of video using learning-based logo verification. Experimental results show that the proposed method achieves improved detection performance as compared with the existing method, and thus can be effectively used for classifying the video.
  • Keywords
    feature extraction; image classification; video watermarking; automatic logo detection; cloud services; commercial video; learning-based verification; logo detection method; video classification; visible watermark; SVM; Video classification; copyright protection; feature extraction; logo detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Consumer Electronics ??? Berlin (ICCE-Berlin), 2014 IEEE Fourth International Conference on
  • Conference_Location
    Berlin
  • Type

    conf

  • DOI
    10.1109/ICCE-Berlin.2014.7034299
  • Filename
    7034299