Sebastian Gerke

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Object-oriented programming is the current mainstream programming paradigm but existing RDF APIs are mostly triple-oriented. Traditional techniques for bridging a similar gap between relational databases and object-oriented programs cannot be applied directly given the different nature of Semantic Web data, for example in the semantics of class membership,(More)
When creating Semantic Web data, users have to make a critical choice for a vocabulary: only through shared vocabularies can meaning be established. A centralised policy prevents terminology divergence but would restrict users needlessly. As seen in collaborative tagging environments , suggestion mechanisms help terminology convergerce without forcing(More)
In this paper we present our approach to the 2010 ImageClef PhotoAnnotation task. Based on the well-known bag-of-words approach we suggest two extensions. First, we analyzed the impact of category specific features and classifiers. In order to classify quality-related image categories we implemented a sharpness measure and use this as additional feature in(More)
In this paper we present our approach to the 2011 ImageClef PhotoAnnotation task, which is based on the well known bag-of-words model. We investigated an approach for selecting the most informative training samples per concept for classification and the impact of fusing the OpponentSIFT feature with the GIST feature which calculates global image statistics,(More)
Currently, bag-of-words approaches for image categorization are very popular due to their relative simplicity, robustness and high efficiency. However, they lack the ability to represent the spatial composition of an image. This drawback has been addressed by several approaches, with spatial pyramids being the most popular. Spatial pyramids divide an image(More)
Player detection in sports video is a challenging task: In contrast to typical surveillance applications, a pan-tilt-zoom camera model is used. Therefore, simple background learning approaches cannot be used. Furthermore, camera motion causes severe motion blur, making gradient based approaches less robust than in settings where the camera is static. The(More)
Video segmentation is an important task for a wide range of applications like content-based video coding or video retrieval. In this paper, a new spatio-temporal video segmentation framework is presented. It is based upon robust statistics, namely an M-estimator, and incorporates an MPEG-7 descriptor for consistent temporal labeling of identified textures.(More)