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Automatic fine-grained hyperlinking of videos within a closed collection using scene segmentation

: Apostolidis, E.; Mezaris, V.; Sahuguet, M.; Huet, B.; Červenková, B.; Stein, D.; Eickeler, S.; Redondo Garcia, J.L.; Troncy, R.; Pikora, L.


Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Multimedia -SIGMM-:
ACM 22nd International Conference on Multimedia, MM 2014. Proceedings : November 3-7, 2014, Orlando, Florida, USA
New York: ACM, 2014
ISBN: 978-1-4503-3063-3
International Conference on Multimedia (MM) <22, 2014, Orlando/Fla.>
Fraunhofer IAIS ()

This paper introduces a framework for establishing links between related media fragments within a collection of videos. A set of analysis techniques is applied for extracting information from different types of data. Visual-based shot and scene segmentation is performed for defining media fragments at different granularity levels, while visual cues are detected from keyframes of the video via concept detection and optical character recognition (OCR). Keyword extraction is applied on textual data such as the output of OCR, subtitles and metadata. This set of results is used for the automatic identification and linking of related media fragments. The proposed framework exhibited competitive performance in the Video Hyperlinking sub-task of MediaEval 2013, indicating that video scene segmentation can provide more meaningful segments, compared to other decomposition methods, for hyperlinking purposes.