  Video abstraction has become one of the efficient approaches to grasp the
content of a video without seeing it entirely. Key frame-based static video
summarization falls under this category. In this paper, we propose a
graph-based approach which summarizes the video with best user satisfaction. We
treated each video frame as a node of the graph and assigned a rank to each
node by our proposed VidRank algorithm. We developed three different models of
VidRank algorithm and performed a comparative study on those models. A
comprehensive evaluation of 50 videos from open video database using objective
and semi-objective measures indicates the superiority of our static video
summary generation method.
