Visual information seeking: tight coupling of dynamic query filters with starfield displays
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interaction in Content-Based Image Retrieval: The Evaluation of the State-of-the-Art Review
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Successful approaches in the TREC video retrieval evaluations
Proceedings of the 12th annual ACM international conference on Multimedia
The use and utility of high-level semantic features in video retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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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.