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This work reports the methodology that CERTH-ITI team developed so as to recognize the emotional impact that movies have to its viewers in terms of valence/arousal and fear. More Specifically,deep convolutional neural networks and several machine learning techniques are utilized to extract visual features and classify them based on the predicted model, while audio features are also taken into account in the fear scenario, leading to highly accurate recognition rates.
MediaEval, emotion detection, movies video, Visual and audio analysis, MediaEval, emotion detection, movies video, Visual and audio analysis
MediaEval, emotion detection, movies video, Visual and audio analysis, MediaEval, emotion detection, movies video, Visual and audio analysis
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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