A three-level framework for affective content analysis and its case studies

Publisher:
Springer
Publication Type:
Journal Article
Citation:
Multimedia Tools And Applications, 2012, 65 pp. 1 - 23
Issue Date:
2012-01
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Emotional factors directly reflect audiences attention, evaluation and memory. Recently, video affective content analysis attracts more and more research efforts. Most of the existing methods map low-level affective features directly to emotions by applying machine learning. Compared to human perception process, there is actually a gap between low-level features and high-level human perception of emotion. In order to bridge the gap, we propose a three-level affective content analysis framework by introducing mid-level representation to indicate dialog, audio emotional events (e.g., horror sounds and laughters) and textual concepts (e.g., informative keywords). Mid-level representation is obtained from machine learning on low-level features and used to infer high-level affective content. We further apply the proposed framework and focus on a number of case studies. Audio emotional
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