The visual concept detection task in ImageCLEF 2008

Abstract

The Visual Concept Detection Task (VCDT) of ImageCLEF 2008 is described. A database of 2,827 images were manually annotated with 17 concepts. Of these, 1,827 were used for training and 1,000 for testing the automated assignment of categories. In total 11 groups participated and submitted 53 runs. The runs were evaluated using ROC curves, from which the Area Under the Curve (AUC) and Equal Error Rate (EER) were calculated. For each concept, the best runs obtained an AUC of 80% or above. © 2009 Springer Berlin Heidelberg.