The performance of video genre classification approaches strongly depends on the selected feature set. Feature selection requires for expert knowledge and is commonly driven by the underlying data, investigated video genres, and previous experience in related application scenarios. An alteration of the genres of interest results in reconsideration of the employed features by an expert. In this work, we introduce an unsupervised method for the selection of features that efficiently represent the underlying data. Performed experiments in the context of audio-based video genre classification demonstrate the outstanding performance of the proposed approach and its robustness across different video datasets and genres.

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