Shot boundary detection using unsupervised clustering and hypothesis testing
We propose a shot boundary detection approach based on unsupervised scenelet clustering and hypothesis testing. We define a video scenelet as a short consecutive samples of frames of a video sequence. The approach makes use of a typical k-means clustering algorithm to group the scenelets. Based on the clustering result, hypothesis testing can be performed to identify the shot boundaries at each level with a different cluster number. Combined with a cluster validity analysis to decide a suitable number of clusters, promising results can be obtained for shot boundary detection.