Overview of the CLEF 2009 Large Scale - Visual Concept Detection and Annotation Task

The large-scale visual concept detection and annotation task (LS-VCDT) in ImageCLEF 2009 aims at the detection of 53 concepts in consumer photos. These concepts are structured in an ontology which implies a hierarchical ordering and which can be utilized during training and classification of the photos. The dataset consists of 18.000 Flickr photos which were manually annotated with 53 concepts. 5.000 photos were used for training and 13.000 for testing. Altogether 19 research groups participated and submitted 73 runs. Two evaluation paradigms have been applied, the evaluation per concept and the evaluation per photo. The evaluation per concept was performed by calculating the Equal Error Rate (EER) and the Area Under Curve (AUC). For the evaluation per photo a recently proposed hierarchical measure was utilized that takes the hierarchy and the relations of the ontology into account and calculates a score per photo. For the concepts, an average AUC of 84% could be achieved, including concepts with an AUC of 95%. The classification performance for each photo ranged between 69% and 100% with an average score of 90%.