Cut is a common type of shot boundary. In previous literature, frame pair similarity (FPS) was usually used as building block of cut detector. For a given frame, one needs to determine what frame pairs in its adjacency to select and how to combine the FPS values. To do these, previous methods, including Cross Analysis, Full Analysis, Graph Cut, etc, resort to human experience and thus lead to inferior performance. Besides, they are susceptible to noise caused by flashlight or popup subtitle such that additional procedure is needed to suppress such noise. In this paper we propose a novel framework to address these problems. Both the frame pair selection and the similarity values combination are done via machine learning. Features insensitive to flashlight or popup subtitle are extracted by exploiting the color histogram based FPS. Experimental results on TRECVID2003~2005 datasets demonstrate the effectiveness of the proposed algorithm.
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