BPNN algorithm towards shot boundary detection
基于BP神经网络的镜头边界检测

The authors presented a neural networks based approach of shot boundary detection by using multi-video features. Two approaches, based on feature differences between two adjacent frames and shifting window respectively, were employed to detect abrupt transition, and motion information was used to reduce the influence of strong movement of objects. The fusion and voting techniques were exploited in the final decision stage. In gradual change detection, three patterns of the variance curve of intensity during the dissolving period were distinguished using three neural networks respectively. Then the interference was eliminated according to the characteristics of linear increasing or decreasing of the mean value of intensity during dissolving interval. Experimental results on TRECVID database indicate that the proposed approach works well in detecting shot boundary measured by both recall and precision, and it is also robust to motion and flash light.