Abstract
AbstractA favorable difference metric is crucial to the shot boundary detection (SBD) performance. In this paper, we propose a new set of metrics, information theoretic metrics, to quantitatively measure the changes between frames. It includes image entropy difference, joint entropy, conditional entropy, mutual information and divergence. They all can be used to cut detection. Specially, the image entropy and joint entropy are good clues to fade detection, while mutual information, joint entropy and conditional entropy are less sensitive to illumination variations. The theoretic analysis and experimental results show that they are useful in SBD.KeywordsMutual InformationVideo SegmentConditional EntropyDiscrete Random VariableIllumination VariationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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