Michael G. Christel
Neema Moraveji
Chang Huang
ABSTRACT
This paper investigates the level of metadata accuracy required for image filters to be valuable to users. Access to large digital image and video collections is hampered by ambiguous and incomplete metadata attributed to imagery. Though improvements are constantly made in the automatic derivation of semantic feature concepts such as indoor, outdoor, face, and cityscape, it is unclear how good these improvements should be and under what circumstances they are effective. This paper explores the relationship between metadata accuracy and effectiveness of retrieval using an amateur photo collection, documentary video, and news video. The accuracy of the feature classification is varied from performance typical of automated classifications today to ideal performance taken from manually generated truth data. Results establish an accuracy threshold at which semantic features can be useful, and empirically quantify the collection size when filtering first shows its effectiveness.
- Ahlberg, C. and Shneiderman, B. Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays. Proc. CHI '94, ACM Press, 313--317. Google Scholar
Digital Library
- NIST TREC Video Retrieval Evaluation, 2001-current, http://www-nlpir.nist.gov/projects/trecvid/.Google Scholar
- Worring, M., Smeulders, A.W.M, and Santini, S. Interaction in content-based retrieval: an evaluation of the state-of-the-art. LNCS 1929, Springer-Verlag (2000), 26--36. Google ScholarDigital Library
Index Terms
- Evaluating content-based filters for image and video retrieval
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