This paper looks into a new direction in video content analysis - the representation and modeling of affective video content . The affective content of a given video clip can be defined as the intensity and type of feeling or emotion (both are referred to as affect) that are expected to arise in the user while watching that clip. The availability of methodologies for automatically extracting this type of video content will extend the current scope of possibilities for video indexing and retrieval. For instance, we will be able to search for the funniest or the most thrilling parts of a movie, or the most exciting events of a sport program. Furthermore, as the user may want to select a movie not only based on its genre, cast, director and story content, but also on its prevailing mood, the affective content analysis is also likely to contribute to enhancing the quality of personalizing the video delivery to the user. We propose in this paper a computational framework for affective video content representation and modeling. This framework is based on the dimensional approach to affect that is known from the field of psychophysiology. According to this approach, the affective video content can be represented as a set of points in the two-dimensional (2-D) emotion space that is characterized by the dimensions of arousal (intensity of affect) and valence (type of affect). We map the affective video content onto the 2-D emotion space by using the models that link the arousal and valence dimensions to low-level features extracted from video data. This results in the arousal and valence time curves that, either considered separately or combined into the so-called affect curve, are introduced as reliable representations of expected transitions from one feeling to another along a video, as perceived by a viewer.
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