A Theoretical Framework And Implementation For Video Segmentation

Shot Boundary detection (SBD) is the basis for video retrieval. With the rapid development of video editing technology, we need new video processing techniques to cope with the new video editing techniques. One of the new challenges was posed by the emerging techniques ,such as the sub-window technique in news video, the traditional methods of video segmentation cannot efficiently detect the video shot boundary caused by special video technique.In this paper, by studying different types of transitions in different resolutions, it can be observed that the shot boundary detection is a temporal multi-resolution phenomenon. From this view, an improved temporal multi-resolution analysis algorithm was developed for video segmentation by using Canny wavelet and applying multi-temporal-resolution analysis on the video stream. The solution is a general framework for all kinds of video transition. We proposed Blocked Chromatic Histogram as a feature vector, the video frame series have the temporal multi-resolution characteristics of shot presented by the wavelet transition coefficients. We use the SVM classifier for pattern recognition with a sliding window to dynamically classify the video frames into normal frames, gradual transition frames and CUT frames, then we clustering the classified frames into different shot categories.The testing result of the experiment on news video clips'truth bed, which has about 30 hours (15 253 shots) in all, shows that the new method has relatively better performance for the SBD. The system is able to improve the precision of the SBD while retaining high recall. The method can resist the influences of sub-window. So it basically resolves the difficulties of shot boundaries caused by sub-window technique in news video clips. It also greatly improved performance of locating the positions of start and end points of GTs.