A Graph-based Ranking Approach to Extract Key-frames for Static Video Summarization
This work addresses the problem of efficiently summarizing videos for users, but it appears incremental as it builds on existing graph-based methods for key-frame extraction.
The authors tackled static video summarization by proposing a graph-based ranking approach, the VidRank algorithm, which achieved superior performance on 50 videos from an open database as measured by objective and semi-objective metrics.
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.