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.-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.