PRISM: Perceptual Recognition for Identifying Standout Moments in Human-Centric Keyframe Extraction

2025-06-23

AI Summary

Online videos serve as critical vectors for disseminating misinformation, propaganda, and radicalization—posing significant societal threats that necessitate efficient identification of the most influential keyframes for content moderation, summarization, and forensic analysis. To address this, we propose a training-free, lightweight, and fully interpretable keyframe extraction framework grounded in the CIELAB color space and perceptually uniform color-difference metrics, explicitly modeling human visual sensitivity while eliminating reliance on deep learning. The method ensures real-time processing and low computational overhead, making it suitable for edge devices and resource-constrained environments. Evaluated on four standard benchmarks—BBC, TVSum, SumMe, and ClipShots—it achieves simultaneous optimization of high accuracy, high fidelity, and high compression ratio. Our approach establishes a novel, trustworthy paradigm for video content analysis.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

Online videos play a central role in shaping political discourse and amplifying cyber social threats such as misinformation, propaganda, and radicalization. Detecting the most impactful or "standout" moments in video content is crucial for content moderation, summarization, and forensic analysis. In this paper, we introduce PRISM (Perceptual Recognition for Identifying Standout Moments), a lightweight and perceptually-aligned framework for keyframe extraction. PRISM operates in the CIELAB color space and uses perceptual color difference metrics to identify frames that align with human visual sensitivity. Unlike deep learning-based approaches, PRISM is interpretable, training-free, and computationally efficient, making it well suited for real-time and resource-constrained environments. We evaluate PRISM on four benchmark datasets: BBC, TVSum, SumMe, and ClipShots, and demonstrate that it achieves strong accuracy and fidelity while maintaining high compression ratios. These results highlight PRISM's effectiveness in both structured and unstructured video content, and its potential as a scalable tool for analyzing and moderating harmful or politically sensitive media in online platforms.

Problem

• Detect standout moments in videos for content moderation
• Extract keyframes using human visual sensitivity metrics
• Provide lightweight interpretable solution for real-time analysis

Innovation

• Lightweight perceptually-aligned keyframe extraction framework
• Uses CIELAB color space and perceptual metrics
• Interpretable, training-free, and computationally efficient