Abstract
In this paper, we propose a 3D fuzzy GIST to effectively describe the visual dynamic features related to the emotional characteristics in a movie clip. Unlike the previous fuzzy approaches, which use images, the proposed method employs movie clips and can dynamically extract the features to classify the emotional characteristics in a movie clip. The 3D fuzzy GIST based on 3D tensor data including L*C*H color (L: Lightness, C: Chroma, H: Hue) and orientation information in a movie clip can extract the visual dynamic features related to the emotional characteristics in a movie clip. The extracted visual dynamic features obtained by the proposed 3D fuzzy GIST are used as inputs to an adaptive neuro-fuzzy inference classifier. The classifier is provided with the mean opinion scores as the teaching signals. Experimental results show that the system with the proposed 3D fuzzy GIST feature extractor not only discriminates the positive emotional features from the negative ones but also identifies the changes of emotional features in movie clips successfully.
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Kwon, M., Lee, M. (2012). 3D Fuzzy GIST to Analyze Emotional Features in Movies. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_24
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DOI: https://doi.org/10.1007/978-3-642-32639-4_24
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