Abstract
In this work, several approaches to feature extraction on sets of time-based events will be developed and evaluated. On the one hand, these sets of events will be extracted from video files and on the other hand it will be manually annotated. By using methods of supervised machine learning the two sets of events will be mapped onto each other. After that, per time slot and requested event type, a binary classification will be applied. Thus aspects of data mining and media technology will be discussed and combined with the goal to reach a reasonable reduction of the input-set by projecting it on an output-set. This will save operator-time in an automated process environment for quality control of audiovisual files. It can be shown, that this objective can be achieved by applying the developed methods. In addition to that, further results and limitations will be presented.
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Edelkamp, S., Jacob, F. (2016). Learning Event Time Series for the Automated Quality Control of Videos. In: Friedrich, G., Helmert, M., Wotawa, F. (eds) KI 2016: Advances in Artificial Intelligence. KI 2016. Lecture Notes in Computer Science(), vol 9904. Springer, Cham. https://doi.org/10.1007/978-3-319-46073-4_13
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DOI: https://doi.org/10.1007/978-3-319-46073-4_13
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