Affiliation:
1. Intel Labs China, China
2. Intel Labs China, China & Tsinghua University, China
Abstract
Video summary is very important for users to grasp a whole video’s content quickly for efficient browsing and editing. In this chapter, we propose a novel video summarization approach based on redundancy removing and content ranking. Firstly, by video parsing and cast indexing, the approach constructs a story board to let user know about the main scenes and the main actors in the video. Then it removes redundant frames to generate a “story-constraint summary” by key frame clustering and repetitive segment detection. To shorten the video summary length to a target length, “time-constraint summary” is constructed by important factor based content ranking. Extensive experiments are carried out on TV series, movies, and cartoons. Good results demonstrate the effectiveness of the proposed method.
Reference22 articles.
1. Bailer, W., Lee, F., & Thallinger, G. (2007). Skimming Rushes Video Using Retake Detection. In Proc. of the TRECVID Workshop on Video Summarization (TVS'07). ACM Multimedia.
2. Gao, Y., Wang, T., Li, J.G., et al. (2007). Cast Indexing for Videos by NCuts and Page Ranking. ACM CIVR 2007.
3. Gong, Y. H., & Liu, X. (2001). Video Summarization with Minimal Visual Content Redundancies. IEEE Proc. of ICIP.
4. Hauptmann, A. G., Christel, M. G., Lin, W., et al. (2007). Clever Clustering vs. Simple Speed-Up for Summarizing BBC Rushes. In Proc. of the TRECVID Workshop on Video Summarization (TVS'07). ACM Multimedia.