電影是一種具有複雜且豐富內容的影片，電影的分析比起監視系統的影片，運動影片或者是紀錄片的分析來得複雜許多。本論文提出以隱藏式馬可夫模型方法將電影的場景分類；本論文將分析電影中兩種常出現的場景：對話場景與動作場景。從電影場景中的每個畫面中抽取顏色及動態特徵。這些從連續畫面中抽取的特徵，將為一時間序列，並且被高斯混合隱藏式馬可夫模型所學習與辨認。在論文實驗中，由兩部電影共有41個對話場景及15個動作場景被實驗，最高的準確率可達80%。

Movie is a kind of complex video with rich content. The analysis of movie is more complicated than other types of videos like surveillance, sport games, and documentaries. In this paper, a statistical approach using hidden Markov model to classify movie scenes is proposed. Two important kinds of movie scenes, dialogue and fighting scenes, are classified. Color and motion features are extracted for each frame. Features of all frames within a scene are regarded as a time series of observations that are statistically modeled by Gaussian mixture ergodic hidden Markov model. Two movies with 41 dialogue scenes and 15 fighting scenes are experimented. The highest accuracy rate can achieve 80%.

目錄
中文摘要 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧i
英文摘要 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ii
誌謝 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧iii
目錄 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧iv
表目錄 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧vi
圖目錄 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧vii
第一章 緒論 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧1
1.1 研究動機與背景 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧1
1.2 相關研究 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧2
1.3 論文方法與論文架構圖 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧5
1.4 論文章節架構 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧7
第二章 場景定義 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧8
2.1 對話場景分析與定義 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧12
2.2 中國動作場景分析與定義 ‧‧‧‧‧‧‧‧‧‧‧‧‧18
第三章 電影內容分析所使用的特徵抽取 ‧‧‧‧‧‧‧‧‧23
3.1 顏色特徵 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧24
3.2 動態特徵 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧27
第四章 電影內容分析所使用的隱藏式馬可夫模型 ‧‧‧‧‧‧34
4.1 隱藏式馬可夫模型介紹 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧34
4.2 電影內容分析所使用的隱藏式馬可夫模型 ‧‧‧‧‧‧46
第五章 實驗與討論 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧48
5.1 實驗結果 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧50
5.2 實驗討論 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧53
第六章 結論與未來研究方向 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧54
參考文獻 ‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧55

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