A fast clustering algorithm for video abstraction
Suyeon Lee, Monson H. Hayes III · 2004
This paper introduces a useful property of the singular value decomposition (SVD) and uses it to quickly summarize a video sequence based on the visual similarities of its frames. In our method, a video is expressed as the representative frames extracted by a simple key-frame extraction algorithm applied in a sequential manner. Then those key-frames are put together with little redundancy using a clustering algorithm for video abstraction. In order to evaluate the proposed scheme, the speed of the commonly used k-means algorithm for clustering is compared with that of the proposed method that combines both the SVD and the k-means algorithm. Experimental results show that our algorithm is fast and effectively summarizes the content of a video with little redundancy.