Content-Based Video Copy Detection

With the development of the Internet technology,people can record and upload videos in anywhere and anytime,which results in large-scale growth of online video data.At the same time,piracy and patent infringement of videos also constrain the growth of video.In order to detect the similar videos efficiently,content-based video copy detection came into being.Traditional content-based video copy detection methods use local features or image features to describe videos.Traditional methods sparsely sample the key frame to decrease the memory usage and time consuming.But the abandoned video frames also include a lot of visual information,which will make an inaccurate retrieval result.In order to improve the shortcomings of the traditional method in speed,we propose a series of improvements to make it better.We use the big vocabulary method and quick strategy of hamming distance computation to speed up the retrieval stage.Then we use the weak geometric consistency to constraint the matched video sequence to improve the accuracy rate.Finally,we get a method with super-fast speed and competitive accuracy rate.In addition,we try to fundamentally solve the problem of matching accuracy and speed.We propose a video copy detection method based on compact video representation.The convolution neural network is used to extract the visual information and the sparse coding is used to retain the key information.And we fuse the features of frame into a compact video feature to reduce the usage of memory and CPU.The compact video feature represents a short time interval video and keep compact enough.Compared with other methods,our method increases both the recall rate and accuracy rate.