Research On Video Near Repetitive Detection Algorithm Based On Local Key Points

The video is the most information-rich carrier with the rapid Development of network technology, interaction and information transfer mode from the most traditional letters and paper slowly migrate to the pictures, voice and video. With the development of digital video technology and network transmission technology, coupled with the ever-changing modern social networking, the amount of digital video data on the network exponentially grows. How these networks on the massive video retrieval, management and copyright protection became a large problem. Which a more prominent issue is that there are a large number of these networks video nearly repeated the (near-duplicate) video. Such a large number of duplicate video not only waste valuable storage space, but it also brings a lot of inconvenience for later retrieval and maintenance. For The video near repeat retrieve not only has far-reaching academic value, but also bring considerable economic value, can improve network video management, retrieval and browsing efficiency, and can also provide news image tracking, copyright protection, and even commercial search The engines have provided nearly repeated image retrieval technology based on pictures and video and video search.This paper first introduces the history of the development of the various parts of the algorithm under the existing framework, then another way to combine these algorithms into a new framework and application of the video to solve near-duplicate retrieval problems, this algorithm framework and now Some algorithms make a comparison, and to clarify their respective strengths and weaknesses. The main work of this paper is as Co I lows:First, brief the basic framework of the existing video near-duplicate detection theory, research status and research directions, taken from video near-duplicate detection theory framework fairly detailed; Such as video frames, key frame, Jens and scene segmentation. Classic algorithms then pick out the highlights, and explain its flaws. Second, the second key point features (key point feature) to make a focus on description. Including its algorithm framework, historical development, which classical critical point feature extraction algorithm, as well as its flaws and places to be improved.Third, Local feature is an isolated feature, which has a good anti-jamming capability, but it lost between the characteristics and features of the global, so many of the researchers later linked to local characteristics of organizations globally. The third part interested in this piece of research work to be a simple review.Fourth, the use of the local characteristics of the key points and the new features of structure and organization framework, a new video near-duplicate detection algorithm based on local key point features. Compared with previous classical algorithms and existing algorithms.