臺灣博碩士論文加值系統

視頻拷貝檢測技術大多都使用特徵點的資訊去做匹配,但如何快速又準確的匹配依然是一個很大的議題.本論文使用一個相當熱門的稀疏編碼技術,把序列中的每張圖經由稀疏特徵編碼,然後訓練出能代表整張圖特徵的字典,再以序列對字典的描述進行的匹配,最後得到的係數在做最後的相似度計算.實驗的結果,大大減少以往的特徵點匹配所需的運算時間,再對各類的攻擊下依然有高準確率,證明了使用稀疏編碼對視頻拷貝的可行性.

In this thesis, to efficiently detect video copies, focus of interests in videos is first localized based on 3D spatiotemporal visual attention modeling. Salient feature points are then detected in visual attention regions. Prior to evaluate similarity between source and target video sequences using feature points, geometric constraint measurement is employed for conducting bi-directional point matching in order to remove noisy feature points and simultaneously maintain robust feature point pairs. Consequently, video matching is transformed to frame-based time-series linear search problem. In addition, for performance comparison, sparse coding is selected to learn representative dictionary for measuring similarity between video sequences. Our proposed approach achieves promising high detection rate under distinct video copy attacks and thus shows its feasibility in real-world applications.

List of Figures 4
List of Table 5
Chapter 1.Introduction 6
Part Ⅰ 9
Chapter 2.Visual Attention Modeling 9
2.1.Detection of spatiotemporal salient points 9
2.2.Motion Attention Map 11
2.3.Visual Attention Modeling 12
Chapter 3.Geometric-Constraint Feature Points Measurement13
Chapter 4.Video Similarity Measurement 14
Part Ⅱ 15
Chapter 5.Proposed Feature-Based Sparse Representation For Video Similarity Assessment Methodology 15
5.1.Dictionary Feature Extraction 15
5.2.Sparse Representation-Based Video Similarity
Assessment 17
Chapter 6.Hough transform correction 18
Chapter 7.Experimental results 20
Chapter 8.Conclusion 24
References 24

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