Graph-based multilevel temporal segmentation of scripted content videos

This paper concentrates on a graph-based multilevel temporal segmentation method for scripted content videos. In each level of the segmentation, a similarity matrix of frame strings, which are series of consecutive video frames, is constructed by using temporal and spatial contents of frame strings. A strength factor is estimated for each frame string by using a priori information of a scripted content. According to the similarity matrix reevaluated from a strength function derived by the strength factors, a weighted undirected graph structure is implemented. The graph is partitioned to clusters, which represent segments of a video. The resulting structure defines a hierarchically segmented video tree. Comparative performance results of different types of scripted content videos are demonstrated.