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This study explores scene recognition in TV series using neural networks, tested on The Big Bang Theory, with various layers like LSTM and pooling methods.
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The input consists of video files and a text file. The video files are divided into independent episodes. The textfile is contains manually created metainformation about every scene. The scene is understand as sequence of frames, that are not interrupted by another frame with different scene location label.