A Graph-based Ranking Approach to Extract Key-frames for Static Video Summarization

TL;DR

This paper introduces a graph-based method called VidRank for static video summarization, which ranks frames to generate summaries that maximize user satisfaction, outperforming existing methods.

Contribution

The paper presents a novel VidRank algorithm with three models for key-frame extraction, demonstrating improved summarization quality through comprehensive evaluation.

Findings

VidRank outperforms existing methods in objective measures.

Three VidRank models show varying effectiveness.

Evaluation on 50 videos confirms method's superiority.

Abstract

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.