Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
International Journal of Computer Vision
Media Meets Semantic Web --- How the BBC Uses DBpedia and Linked Data to Make Connections
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Foundations and Trends in Information Retrieval
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Towards exploratory video search using linked data
Multimedia Tools and Applications
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Rapid growth of today's video archives along with sparsely available editorial metadata and too few capacities of libraries and archives for manual annotation demand for efficient approaches of automated metadata extraction. In addition, editorial and non-authoritative metadata is usually not fine-grained enough to describe video on a segment level, which is often required for efficient pinpoint search and retrieval. We consider the use case of the AV Portal provided by the German National Library of Science and Technology -- a web based video search engine that offers access to educational video content from various areas of engineering and natural sciences. User studies that have been conducted during the conceptional design stage of the AV Portal have indicated a strong interest of potential users to search for specific visual concepts, like e.g. "landscape", "drawing", "animation", within videos of a particular domain. We present an approach that supports automatic content-based classification of video segments that is tailored to the special requirements of the AV Portal regarding its technology oriented content and academic users. We furthermore show that semantic analysis of the generated metadata not only allows for better retrieval goal definition but also offers explorative search within the archive using visual concepts.