Clustering techniques have been widely used in automatic video-summarization applications to group shots with comparable content. We enhance the popular k-means clustering algorithm to integrate user-supplied domain-knowledge into the cluster generation step. This provides a convenient way to exclude scenes from the summary which are a-priori known to be irrelevant. Furthermore, we added an additional, time-constrained clustering step preceding the scene clustering step to exclude short ranges with transitional content. This makes the algorithm robust to fading and wipe-effects in the input without requiring explicit cut detection.
Dieser Eintrag ist Teil der Universitätsbibliographie.
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