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博碩士論文 etd-1013105-103426 詳細資訊
Title page for etd-1013105-103426
論文名稱
Title
以支援向量分類為依據之影片場景變化偵測
Video Scene Change Detection Using Support Vector Clustering
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
67
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2005-10-10
繳交日期
Date of Submission
2005-10-13
關鍵字
Keywords
支援向量分類、影片場景變化
video scene change, support vector clustering
統計
Statistics
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中文摘要
隨著數位化時代來臨,大量多媒體資料(圖像、影片等)被數位化儲存於資料庫中,而其衍生之檢索系統也越來越重要。而影片資料量龐大,為了能夠有效並快速檢索,第一步會先偵測影片中場景改變之處,將場景分割,再從各個場景中找出關鍵影格,以關鍵影格當作索引檔來進行檢索之分析。
場景改變方式分成突然場景變化和緩慢場景變化兩大類,但在影片中,即使是相同場景,也常有劇烈動作或是鏡頭移動等事件發生,而與緩慢場景變化有所混淆,因此本論文以主成份分析法(PCA)將影片中每一影格之主成分取出來當作特徵,降低雜訊干擾,再對特徵點進行支援向量分類,相近之特徵點被歸為同一類,如果特徵點位於兩群不同資料之間,代表特徵點對應之影格,在影片中正處於緩慢場景變化過程中,藉此偵測出場景變化。
Abstract
As digitisation era will come, a large number of multimedia datas (image, video, etc.) are stored in the database by digitisation, and its retrieval system is more and more important. Video is huge in frames amount, in order to search effectively and fast, the first step will detect and examine the place where the scene changes in the video, cut apart the scene, find out the key frame from each scene, regard as analysis that the index file searches with the key frame.


The scene changes the way and divides into the abrupt and the gradual transition. But in the video, even if in the same scene, incident of often violent movements or the camera are moving etc. happens, and obscure with the gradual transition to some extent. Thus this papper gets the main component from every frame in the video using principal component analysis (PCA), reduce the noise to interfere, and classify these feauture points with support vector clustering, it is the same class that the close feature points is belonged to. If the feature points are located between two groups of different datas, represent the scene is changing slowly in the video, detect scene change by this.
目次 Table of Contents
摘要 1
第一章 簡介 5
第一節 序論 5
第二節 相關研究 12
1.2.1以相鄰影格差值進行分析 12
1.2.2以色彩直方圖 (color histogram)進行分析 14
1.2.3以邊緣像素(edge pixel)進行分析 15
1.2.4以移動向量(motion vector)進行分析 17
1.2.5以相似度比對(Likelihood ratio)進行分析 19
1.2.6計算聯合機率分佈 20
1.2.7以影像特徵作分析 22
1.2.8以線性回歸方法偵測 25
1.2.9以統計模型描述影片內容 27
1.2.10以MPEG之巨區塊(macro-blocks)分析 28
第二章 理論基礎 32
第一節 主成份分析法 (Principal Component Analysis) 32
第二節 支援向量分類 ( Support vector clustering ) 37
第三章 研究方法與步驟 45
第一節 空間轉換 45
第二節 資料分類 47
第四章 實驗數據與討論 54
第一節 效能評估 54
第二節 測試影片 54
第三節 比較方法 56
第五章 結論與未來工作 62
第一節 結論 62
第二節 未來工作 62
參考文獻 64
參考文獻 References
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