Video Segmentation Algorithm Design Based On TMRA And SVM

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 with emerging from the sub-window technique in news video; the original method of video segmentation cannot efficiently detect the video shot boundary caused by special video technique.In this paper, by studying different types of transitions, it can be observed that the shot boundary detection is a temporal multi-resolution phenomenon. An improved temporal multi-resolution analysis (TMRA) algorithm was developed for video segmentation by using Canny wavelet and applying TMRA on the video stream. The solution is a general framework for all kinds of video transitions. We proposed Blocked Chromatic Histogram (BCH) as a feature vector. Traditional statistical method cannot deal with feature vectors with so high dimensions efficiently, but SVM (Supported Vector Machine) could resolve this problem. The proposed algorithm used the SVM classifier for pattern recognition, and a sliding window was used to dynamically classify the video frames into normal frames (NF), gradual transition frames (GF) and CUT frames (CF). Then we clustered the classified frames into different shot categories. In one word, BCH can supply sufficient information in the video clips and SVM can filter the noise in the video automatically, so we can get better detection result.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. Our 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...