Video Summarization based on Nonnegative Linear Reconstruction

With the development of imaging techniques and the Internet, it is hard to effectively and efficiently manage, index and store large amounts of videos. Video summarization appears to this need by obtaining useful information in videos. Recently, the reconstruction concept has been introduced into video summarization. However, most of these existing reconstruction methods where a dictionary of key frames is selected to reconstruct the original video best might come up with redundant information. In this paper, we propose a novel framework named Video Summarization based on Nonnegative Linear Reconstruction (VSNLR) which allows only additive, not subtractive, linear combinations. Our approach consists of two core steps: (1) we detect the shot boundary and choose a representative frame for every shot, then all the representative frames form the candidate set; (2) for every frame in the candidate set, VSNLR selects related frames to reconstruct the given frame by using the nonnegative linear reconstruction function. The key frames are selected by minimizing the sum of reconstruction errors. Experiments on a dataset and comparison to the state-of-art method demonstrate our advantage.