%0 Conference Paper %1 Hentschel:2013:AAS:2494188.2494213 %A Hentschel, Christian %A Blümel, Ina %A Sack, Harald %B Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies %C New York, NY, USA %D 2013 %I ACM %K mypaper semanticanalysis vcd visualanalysis %P 16:1--16:8 %R 10.1145/2494188.2494213 %T Automatic Annotation of Scientific Video Material Based on Visual Concept Detection %U http://doi.acm.org/10.1145/2494188.2494213 %X 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", änimation", 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. %@ 978-1-4503-2300-0

@inproceedings{Hentschel:2013:AAS:2494188.2494213, abstract = {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.}, acmid = {2494213}, added-at = {2014-05-08T11:48:59.000+0200}, address = {New York, NY, USA}, articleno = {16}, author = {Hentschel, Christian and Bl\"{u}mel, Ina and Sack, Harald}, biburl = {https://www.bibsonomy.org/bibtex/2c47503c4cf42bb3d0eb8cfcb9f340710/lysander07}, booktitle = {Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies}, description = {Automatic Annotation of Scientific Video Material based on Visual Concept Detection}, doi = {10.1145/2494188.2494213}, interhash = {77de5143a21a30ee1140155de1439a6e}, intrahash = {c47503c4cf42bb3d0eb8cfcb9f340710}, isbn = {978-1-4503-2300-0}, keywords = {mypaper semanticanalysis vcd visualanalysis}, location = {Graz, Austria}, numpages = {8}, pages = {16:1--16:8}, publisher = {ACM}, series = {i-Know '13}, timestamp = {2014-05-08T11:48:59.000+0200}, title = {Automatic Annotation of Scientific Video Material Based on Visual Concept Detection}, url = {http://doi.acm.org/10.1145/2494188.2494213}, year = 2013 }