A shot boundary detection algorithm based on Particle Swarm Optimization Classifier

Shot boundary detection is always an important topic in digital video processing. It is the first important task of content-based video retrieval and indexing. In this paper, a new shot boundary detection algorithm is proposed, based on particle swarm optimization classifier. This method firstly takes the difference curves of U-component histograms as the characteristics of the differences between video frames, and then utilizes a slide-window mean filter to filter difference curves and a KNN classifier applying PSO to detect and classify the shot transitions. This method has three advantages that it is more sensitive to gradual transitions; each curve graphic with remarkable characteristics corresponds to a shot transition; Cuts and Gradual transitions could be detected in a same step. As experiments shown, the performance of this method is superior to the traditional shot boundary detection methods, and this method can achieve high recall and precision rate.