Abrupt shot change detection using an unsupervised clustering of multiple features
H. Lee, Cheong Woo Lee, Seong Dae Kim · 2002
We propose an efficient method to detect abrupt shot changes in a video sequence by using an unsupervised clustering. Most conventional shot change detection algorithms use only one kind of frame-by-frame difference feature such as pixel difference or histogram difference, so they can be applied to only specific situations. Another problem is the determination of appropriate threshold values to check the existence of shot changes. To overcome these problems we use several kinds of features simultaneously and propose a modified k-means clustering algorithm which changes the initial cluster center adaptively. Experimental results show that the proposed algorithm works well.