| Summary: | 碩士 === 國立政治大學 === 資訊科學系 === 106 === Film score is essential to movies. Composers compose background scores for movies according to movie styles and genres. Much research has been done on video content analysis, but none has been done on timing prediction of movie score. In this thesis, we investigate the timing prediction of film score based on data mining techniques. It is helpful for timing prediction of background music for user generated content.
In the proposed approach, the timing prediction problem is transformed as a binary classification problem. We first segment movies into scenes by alignment between scripts and subtitles of movies. After movie segmentation, visual features, text features, movie metadata and sentiment features of each scene are extracted and are used to learn the prediction model. In the experiments, Decision Tree, Logistic Regression, Support Vector Machine, Random Forest and Conditional Random Field algorithms are employed for model training. The result of experiments show that timestamp, proportion of subtitles and word density of scenes are key factors of timing prediction and taking context into consideration can improve prediction performance.
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