Bayesian Video Shot Segmentation

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

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Nuno Vasconcelos, Andrew Lippman


Prior knowledge about video structure can be used both as a means to improve the peiformance of content analysis and to extract features that allow semantic classification. We introduce statistical models for two important components of this structure, shot duration and activity, and demonstrate the usefulness of these models by introducing a Bayesian formulation for the shot segmentation problem. The new formulations is shown to extend standard thresholding methods in an adaptive and intuitive way, leading to improved segmentation accuracy.