[Paper Review] A Graph-based Ranking Approach to Extract Key-frames for Static Video Summarization

Saikat Chakraborty|arXiv (Cornell University)|Nov 29, 2019

Video Analysis and Summarization4 citations

TL;DR

This paper proposes a graph-based ranking method, VidRank, to extract key-frames for static video summarization by modeling video frames as nodes in a graph and assigning them ranks based on visual and temporal features. The approach outperforms existing methods in objective and semi-objective evaluations across 50 videos from an open database, demonstrating superior user satisfaction and summarization quality.

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.

Motivation & Objective

• To develop a graph-based approach for static video summarization that enhances user satisfaction by selecting representative key-frames.
• To design and evaluate multiple variants of the VidRank algorithm for frame ranking using visual and temporal features.
• To compare the performance of different VidRank models using objective and semi-objective metrics on a diverse video dataset.
• To validate the effectiveness of the proposed method through comprehensive evaluation on a benchmark video database.

Proposed method

• Each video frame is represented as a node in a graph, with edges weighted based on visual and temporal similarity between frames.
• The VidRank algorithm computes node importance using a graph-based ranking mechanism inspired by PageRank, incorporating both visual and temporal features.
• Three distinct models of VidRank are developed, differing in feature representation and weighting schemes for visual and temporal cues.
• Frame ranking is computed iteratively using a random walk with restarts, where the probability distribution converges to a stable state reflecting frame importance.
• The top-ranked frames are selected as key-frames to form a static video summary.
• Evaluation uses both objective metrics (e.g., F1-score, precision) and semi-objective measures to assess summarization quality.

Experimental results

Research questions

• RQ1 How does the graph-based ranking approach compare to existing key-frame extraction methods in terms of summarization quality?
• RQ2 What is the impact of different feature combinations (visual and temporal) on frame ranking performance?
• RQ3 Which VidRank model variant achieves the highest consistency and accuracy in identifying representative key-frames?
• RQ4 To what extent does the proposed method improve user satisfaction compared to baseline approaches?

Key findings

• The proposed graph-based VidRank method achieved superior performance across multiple objective and semi-objective evaluation metrics on a dataset of 50 videos.
• The best-performing VidRank model outperformed baseline methods in F1-score, precision, and recall, indicating higher accuracy in key-frame selection.
• The integration of both visual and temporal features in the ranking mechanism significantly improved the quality of the generated summaries.
• The method demonstrated robustness and consistency across diverse video content, including scenes with varying motion and visual complexity.