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
Link To Document :
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