An Efficient Content and Segmentation Based Video Copy Detection
Keywords:
Transformation, matching content, encodingAbstract
The field of multimedia technology has become easier to store, creation and access large amount of video data. This technology has editing and duplication of video data that will cause to violation of digital rights. So in this project we implemented an efficient content and segmentation based video copy detection concept to detect the illegal manipulation of video. In this Work or proposed system, Instead of SIFT matching algorithms, used combination of SIFT and SURF matching algorithms to detect the matching features in images. Because, SIFT is slow and not good at illumination changes, while it is invariant to rotation, scale changes and affine transformations and then SURF is fast and has good performance, but it is also have some issues that it is not stable to rotation and affine transformations. So combined the above two algorithms SIFT and SURF to extract the image features. Auto dual Threshold method is used to segment the video into segments and extract key frames from each segment and it also eliminate the redundant frame. SIFT and SURF features based on SVD is used to compare the two frames features sets points, where the SIFT and SURF features are extracted from the key frames of the segments. Graph-based video sequence matching method is used to match the sequence of query video and train video. It skillfully converts the video sequence matching result to a matching result graph.
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