The University of Amsterdam's Concept Detection System at ImageCLEF 2011

The University of Amsterdam participated in the photo annotation task and the concept-based retrieval task of ImageCLEF 2011. In the per-image evaluation of the photo annotation task, we achieve the highest score overall. For the concept-based retrieval task, we submitted the best visual-only run. For the concept-based retrieval task, we considered three ways to perform visual retrieval: fully automatic, human topic mapping and human topic inspection. For a fully automatic system, including more random negatives to train a topic model improves results. For a human selecting relevant concepts to the topic, multiplication fusion works better than summation. For human topic inspection, a relevance feedback scheme on the train data gives an 8-fold increase in the number of positive examples per topic. Depending on the topic, the human topic mapping (best for 21 topics) and inspection (best for 17 topics) give the best results. An oracle fusion of the different methods would increase MAP from 0.100 for our best run to 0.128 overall.