A robust learning-based TV commercial detection approach is proposed in this paper. Firstly, a set of basic features that facilitate distinguishing commercials from general program are analyzed. Then, a series of context-based features, which are more effective for identifying commercials, are derived from these basic features. Next, each shot is classified as commercial or general program based on these features by a pre-trained SVM classifier. And last, the detection results are further refined by scene grouping and some heuristic rules. Experiments on around 10-hour TV recordings of various genres show that the proposed scheme is able to identify commercial blocks with relatively high detection accuracy.
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