• DocumentCode
    2935710
  • Title

    Can social tagged images aid concept-based video search?

  • Author

    Setz, Arjan T. ; Snoek, Cees G M

  • Author_Institution
    Intell. Syst. Lab. Amsterdam, Univ. of Amsterdam, Amsterdam, Netherlands
  • fYear
    2009
  • fDate
    June 28 2009-July 3 2009
  • Firstpage
    1460
  • Lastpage
    1463
  • Abstract
    This paper seeks to unravel whether commonly available social tagged images can be exploited as a training resource for concept-based video search. Since social tags are known to be ambiguous, overly personalized, and often error prone, we place special emphasis on the role of disambiguation. We present a systematic experimental study that evaluates concept detectors based on social tagged images, and their disambiguated versions, in three application scenarios: within-domain, cross-domain, and together with an interacting user. The results indicate that social tagged images can aid concept-based video search indeed, especially after disambiguation and when used in an interactive video retrieval setting. These results open-up interesting avenues for future research.
  • Keywords
    social networking (online); video retrieval; concept detectors; concept-based video search; interactive video retrieval; social tagged images; Content based retrieval; Detectors; Humans; Image retrieval; Intelligent systems; Labeling; Machine learning; Scalability; Speech; Tagging;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on
  • Conference_Location
    New York, NY
  • ISSN
    1945-7871
  • Print_ISBN
    978-1-4244-4290-4
  • Electronic_ISBN
    1945-7871
  • Type

    conf

  • DOI
    10.1109/ICME.2009.5202778
  • Filename
    5202778