• DocumentCode
    2558614
  • Title

    SVM Based Classification of Seven Nature Objects for Anytime, Anywhere Digital Photo Annotation

  • Author

    Song, Chull Hwan ; Yoo, Seong Joon

  • Author_Institution
    Sejong Univ., Seoul
  • fYear
    2007
  • fDate
    26-28 April 2007
  • Firstpage
    1249
  • Lastpage
    1254
  • Abstract
    This paper proposes a method that can be utilized for automatically annotating digital photos anytime, anywhere. A digital camera or an annotation server connected to the digital camera through a ubiquitous computing network can automatically annotate captured photos using the proposed method. Annotating digital images is not a new research problem. We have developed a novel method of classifying seven nature objects from digital images. Thus, this paper describes the method and shows that it is superior to previous methods of classifying nature objects.
  • Keywords
    image classification; support vector machines; ubiquitous computing; video cameras; SVM based classification; automatic digital photo annotation; digital camera; digital image annotation; nature object classification; ubiquitous computing network; Digital cameras; Digital images; Histograms; Humans; Internet; Layout; Principal component analysis; Support vector machine classification; Support vector machines; Ubiquitous computing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia and Ubiquitous Engineering, 2007. MUE '07. International Conference on
  • Conference_Location
    Seoul
  • Print_ISBN
    0-7695-2777-9
  • Type

    conf

  • DOI
    10.1109/MUE.2007.199
  • Filename
    4197451