Renmin University of China at ImageCLEF 2013 Scalable Concept Image Annotation

In this paper we describe our image annotation system participated in the ImageCLEF 2013 scalable concept image annotation task. The system leverages multiple base classifiers, including singlefeature and multi-feature kNN classifiers and histogram intersection kernel SVMs, all of which are learned from the provided 250K web images and provided features with no extra manual verification. These base classifiers are combined into a stacked model, with the combination weights optimized to maximize the geometric mean of F-samples, F-concepts, and AP-samples metrics on the provided development set. By varying the configuration of the system, we submitted five runs. Evaluation results show that for all of our runs, model stacking with optimized weights performs best. Our system can annotate diverse Internet images purely based on the visual content, at the following accuracy level: F-samples of 0.290, F-concepts of 0.304, and AP-samples of 0.380. What is more, a system-to-system comparison reveals that our system and the best submission this year are complementary with respect to the best annotated concepts, suggesting the potential for future improvement.