Abstract—Video summarization is an efficient and flexible way to
represent video data. In this paper, we use the Kernel PCA and
clustering based key frame extraction to realize multilevel video
representation. In order to remove the redundancy caused by large
scene changes, SIFT flow scene alignment is performed on the
clustering set of key frames. After alignment, one representative
frame is chosen from the reconstructed cluster set on matched frame pairs. We explore the difference on data structures between frame level and scene level, and modify the FCM method on the cluster number initialization for video summarization. Experimental results are presented to verify the efficiency of our approach.
