政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/119909

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/119909

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/119909

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/119909

Title:	以資料探勘技術預測電影配樂的使用時機 Timing Prediction of Movie Scoring Based on Data Mining Techniques
Authors:	段承甫 Duan, Cheng-Fu
Contributors:	沈錳坤 Shan, Man-Kwan 段承甫 Duan, Cheng-Fu
Keywords:	電影配樂時機預測電影分段資料探勘 Film score Timing prediction Movie segmentation Data mining
Date:	2018
Issue Date:	2018-09-03 15:52:01 (UTC+8)
Abstract:	好的電影配樂是一部傑出電影不可或缺的一部分。音樂家根據電影的類型、風格，在對的時機點，為電影量身打造出適合的配樂。過去已有許多與影片內容分析相關的研究，但尚未有預測電影配樂使用時機的研究。本研究以配樂表現傑出的電影為樣本資料，利用資料探勘技術學習出配樂時機的預測模型，以模型自動為尚未配樂的電影找出適合配樂的電影片段。此研究能延伸於User-Generated Video的背景音樂時機預測。本研究將電影配樂使用時機的預測問題轉換成二元分類問題。為了使電影片段對於配樂的使用具有代表性，我們以場景為單位將電影分段，我們利用電影劇本與電影字幕對齊以及電影鏡頭的資訊將電影分段。電影分段後我們抓取每一個片段的視覺特徵、文字特徵、電影Metadata與其它特徵，以此些特徵訓練預測模型。我們於實驗中以Decision Tree、Logistic Regression、Support Vector Machine、Random Forest與Conditional Random Field進行實驗，從中觀察影響配樂使用時機的關鍵因素與不同電影之間的預測結果，並加上考慮場景情境的條件下是否能提升預測的效果。從實驗結果發現，影響配樂使用時機的主要因素為片段於電影中的時間點、台詞的時間比例與台詞密度。加上考慮場景的情境能提升大部分電影的預測效果，而使用Random Forest作為預測模型的演算法效果最佳(R-Precision約0.663，Area under the Curve of ROC約0.675)。 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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Description:	碩士國立政治大學資訊科學系 104753017
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0104753017
Data Type:	thesis
DOI:	10.6814/THE.NCCU.CS.012.2018.B02
Appears in Collections:	[Department of Computer Science ] Theses

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