Research And Realization Of Video Abstraction

With the popularity of the network and video capture such as the development of multimedia technology, a large number of digital video comes. How to search a desired video clip effectively in a massive video database for saving user's time and effort becomes an important research topic. The problem of "difficult choice" has been around the corner. Video Summary is proposed to overcome the problem. The technology results in video's summary of the main content, Video summary has found its wide applications.In this thesis, the main work that has been done is described as following:The first two chapters are mainly to do some analysis and summarize state-of-the-art of the video abstraction field.The third chapter starts from the video frame difference and improves the dual-threshold method. Dynamic sliding window have a good local adaptation. Mutation or gradient shots often occurs between a few frames. This effectively avoids the drawbacks of global threshold. Threshold superimposed takes into account the relationship video between frames. However, when the starting point is too far away, it will inevitably affect the final result. Dual-threshold method of dynamic sliding window without losing the threshold in both the scientific. Also can adequately address the changes in the local frames. Wheat standards applied to the local double threshold value. After several iterations, near the threshold of experience selected an optimal fluctuation ultimately. The results of the experiment are satisfactory.The fourth chapter introduces the basic mean shift algorithm and the extended mean shift algorithm. Mean shift clustering algorithm is applied to frame of video. The traditional k-means clustering algorithm is a random starting point selection. Resulting in unstable clustering results, and sometimes fall into local minimum solution, and the algorithm requires the user pre-specified number of categories k. The mean shift algorithm, as a non-parametric density estimation algorithm, data sets to identify the peak density, and no pre-determined number of categories, more conducive to large amounts of data clustering. So using mean shift clustering algorithm to video frame. Then put forward to the second classification clustering results, redrawing some categories those boundary frames are far away from each other. Deleting some categories those have a few frames. Then select the middle frame from the final classification as the representative frame to compose video abstraction.The fifth chapter designed and implemented a video abstraction system. The system as long as a raw video input, the output is a list of summary images and all images can be stored. Shot segmentation as a common method of video processing. In the system has also been reflected, for access to the sub-shots can also provide targeted player. In addition, taking into account video'more application, system also increased the extraction of any interval and real-time image grab modules, for facilitating future in-depth study.