• DocumentCode
    1420087
  • Title

    Automatic media data rating based on class probability output networks

  • Author

    Rosas, Harvey ; Kil, Rhee Man ; Han, SeungWan

  • Author_Institution
    Dept. of Math. Sci., Korean Adv. Inst. of Sci. & Technol. (KAIST), Daejeon, South Korea
  • Volume
    56
  • Issue
    4
  • fYear
    2010
  • fDate
    11/1/2010 12:00:00 AM
  • Firstpage
    2296
  • Lastpage
    2302
  • Abstract
    This paper presents a novel method of classifying media data whether they include X-rated contents or not. In our work, the classification of media data is performed using the class probability output network (CPON) which estimates the conditional class probability. Consequently, the classification of media data can be done using the degree of confidence for the class membership, not just using the discriminant value which is usually used in many classification problems. Furthermore, the accuracy of the estimated conditional class probability can be measured in the suggested CPON and this gives a good guideline for the final decision of classification. To demonstrate the effectiveness of the suggested method, the simulation for automatic media rating of the data sampled from multimedia data streams in the Internet was performed. We showed that the suggested CPON-based method provides the better performance than other classifiers using discriminant functions.
  • Keywords
    Internet; media streaming; pattern classification; probability; CPON-based method; Internet; automatic media data rating; class membership; class probability output networks; degree of confidence; media data classification; multimedia data streams; Classification algorithms; Feature extraction; Image color analysis; Kernel; Media; Support vector machines; Training; Media Data Rating, Multimedia Data, Support Vector Machine, Class Probability Output Network.;
  • fLanguage
    English
  • Journal_Title
    Consumer Electronics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0098-3063
  • Type

    jour

  • DOI
    10.1109/TCE.2010.5681103
  • Filename
    5681103