An Efficient Scene-Break Detection Method Based on Linear Prediction With Bayesian Cost Functions

This paper describes an efficient approach to scene-break detection, which can detect cuts, dissolves, and wipes reliably and effectively by means of temporally linear prediction models. In our algorithm, two linear prediction models are adopted to predict a current frame: one for dissolves, and the other for stationary scenes. The predicted frames, derived based on the two models, are compared with the original frames, and cuts and dissolves are then determined based on Bayesian cost functions. For the detection, our algorithm requires the setting of a single threshold only. In wipe detection, our linear prediction models are employed to detect areas of change between two successive frames. By accumulating the changed areas and the overlap of the changed areas over the successive frames, wipes of an arbitrary shape and direction are detected. Experimental results show that our algorithm can achieve a high level of precision even if a video contains object motion and camera motion. The detection time required to analyze a 38-min video is no more than several seconds.

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