A static video summarization method based on hierarchical clustering

Abstract

Video summarization is a simplification of video content for compacting the video information. The video summarization problem can be transformed to a clustering problem, in which some frames are selected to saliently represent the video content. In this work, we use a graph-theoretic divisive clustering algorithm based on construction of a minimum spanning tree to select video frames without segmenting the video into shots or scenes. Experimental results provides a visually comparison between the new approach and other popular algorithms from the literature, showing that the new algorithm is robust and efficient. © 2010 Springer-Verlag.