| Summary: | 碩士 === 元智大學 === 電機工程學系 === 100 === 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.
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