A novel compact yet rich key frame creation method for compressed video summarization

Video summarization has great potential to enable rapid browsing and efficient video indexing in many applications. In this study, we propose a novel compact yet rich key frame creation method for compressed video summarization. First, we directly extract DC coefficients of I frame from a compressed video stream, and DC-based mutual information is computed to segment the long video into shots. Then, we select shots with static background and moving object according to the intensity and range of motion vector in the video stream. Detecting moving object outliers in each selected shot, the optimal object set is then selected by importance ranking and solving an optimum programming problem. Finally, we conduct an improved KNN matting approach on the optimal object outliers to automatically and seamlessly splice these outliers to the final key frame as video summarization. Previous video summarization methods typically select one or more frames from the original video as the video summarization. However, these existing key frame representation approaches for video summarization eliminate the time axis and lose the dynamic aspect of the video scene. The proposed video summarization preserves both compactness and considerably richer information than previous video summaries. Experimental results indicate that the proposed key frame representation not only includes abundant semantics but also is natural, which satisfies user preferences.