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This report documents the program and the outcomes of Dagstuhl Seminar 11261 ``Outdoor and Large-Scale Real-World Scene Analysis, 15th Workshop Theoretic Foundations of Computer Vision''. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general, followed by the scheduled programme. Overall, the seminar was a great success, which is also reflected in the very positive feedback we received from the evaluation.

In recent years, the advent of car navigation systems has laid the ground for an entirely new industry sector, consisting of map producers, car/ personal/ smart phone navigation manufacturers, and service providers. It has probably gone unnoticed that navigation systems mark a major change in the way we use maps. Partially, they are still just a replacement for traditional maps, providing a means to store and visualize a representation of the environment. In contrast to the traditional use of maps, however, navigation systems perform computations using the map's data structures, such as shortest route, map matching, and route guidance. That is, from an abstract point of view, part of the map is made for machine use only – the user has no direct access to it but rather is only presented the outcome of the computations.

This report documents the program and the outcomes of Dagstuhl Seminar 11261 ``Outdoor and Large-Scale Real-World Scene Analysis, 15th Workshop Theoretic Foundations of Computer Vision''. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general, followed by the scheduled programme. Overall, the seminar was a great success, which is also reflected in the very positive feedback we received from the evaluation.

From September 12th to 17th, 2010, the Dagstuhl Seminar 10371 ``Dynamic Maps '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.

Cross-domain learning methods have shown promising results by leveraging labeled patterns from auxiliary domains to learn a robust classifier for target domain, which has a limited number of labeled samples. To cope with the tremendous change of feature distribution between different domains in video concept detection, we propose a new cross-domain kernel learning method. Our method, referred to as Domain Transfer SVM (DTSVM), simultaneously learns a kernel function and a robust SVM classifier by minimizing both the structural risk functional of SVM and the distribution mismatch of labeled and unlabeled samples between the auxiliary and target domains. Comprehensive experiments on the challenging TRECVID corpus demonstrate that DTSVM outperforms existing cross-domain learning and multiple kernel learning methods.

Cross-domain learning methods have shown promising results by leveraging labeled patterns from auxiliary domains to learn a robust classifier for target domain, which has a limited number of labeled samples. To cope with the tremendous change of feature distribution between different domains in video concept detection, we propose a new cross-domain kernel learning method. Our method, referred to as Domain Transfer SVM (DTSVM), simultaneously learns a kernel function and a robust SVM classifier by minimizing both the structural risk functional of SVM and the distribution mismatch of labeled and unlabeled samples between the auxiliary and target domains. Comprehensive experiments on the challenging TRECVID corpus demonstrate that DTSVM outperforms existing cross-domain learning and multiple kernel learning methods.