The Fraunhofer IDMT at image CLEF 2011 photo annotation task

This paper presents the participation of the Fraunhofer IDMT in the ImageCLEF 2011 Photo Annotation Task. Our approach is focused on text-based features and strategies to combine visual and textual information. First, we apply a pre-processing step on the provided Flickr tags to reduce noise. For each concept, tf-idf values per tag are computed and used to construct a text-based descriptor. Second, we extract RGB-SIFT descriptors using the codebook approach. Visual and text-based features are combined, once with early fusion and once with late fusion. The concepts are learned with SVM classifiers. Further, a post-processing step compares tags and concept names to each other. Our submission consists of one text-only and four multi-modal runs. The results show, that a combination of text-based and visual-features improves the result. Best results are achieved with the late fusion approach. The post-processing step only improves the results for some concepts, while others worsen. Overall, we scored a Mean Average Precision (MAP) of 37.1% and an example-based F-Measure (F-ex) of 55.2%.