Graph Partition Based Scene Boundary Detection

Abstract

In this paper a graph partition based scene boundary detection method is proposed. Multiple features extracted from the video are considered for the determination of the scene boundaries in an unsupervised clustering procedure. For each video shot to shot comparison feature, one-dimensional signal is constructed by graph partitions obtained from the similarity matrix in a temporal interval. After each one-dimensional signal is filtered, k-means clustering is conducted for finding scene boundaries. The proposed graph-based scene boundary detection method is evaluated and compared with the graph-based scene detection method presented in literature.