Video Summarization Based on SVD and Sparse Subspace Clustering

In order to make video browsing and storing more convenient, the technique of video summarization whose purpose is summarizing the content of video becomes very important. At present, keyframe numbers determination method can be organized into two categories:the one determining the numbers according to the priori knowledge, and another according to threshold adjustment. However, flexibility and intuition are mostly lacked in both methods. To solve this problem, a video summarization method based on singular value decomposition and sparse subspace clustering is proposed. Firstly, time dimension of the video is reduced by singular value decomposition, and principal component numbers on time dimension which are determined by the cumulative contribution rate are regarded as the keyframe numbers. Secondly, the video frames are clustered by sparse subspace clustering. Finally, in each cluster the frame which has the biggest correlation with other frames is selected as keyframe. Experimental results indicate that the proposed method can generate video summarizations with high content coverage rate, and adjust the lengths of video summarizations flexibly and intuitively according to lengths and types of videos. The range of the cumulative contribution rate of videos with different types and lengths is given, which can provide an effective basis for users to extracting a summary with an appropriate length.