Rajan Manjusha, Parameswaran Latha
Department of Computer Science and Engineering, Amrita School of Computing, Coimbatore, Amrita Vishwa Vidyapeetham, India, 641112.
Sci Rep. 2025 Jan 2;15(1):536. doi: 10.1038/s41598-024-84324-0.
With rapid technological advancements, videos are captured, stored, and shared in multiple formats, increasing the requirement for summarization techniques to enable shorter viewing durations. Key Frame Extraction (KFE) algorithms are crucial in video summarization, compression, and offline analysis. This study aims to develop an efficient KFE approach for generic videos. Existing methods include the Adaptive Key Frame Extraction Algorithm, which reduces redundancy while ensuring maximum content coverage; the Optimal Key Frame Extraction Algorithm, which utilizes a Genetic Algorithm (GA) to select key frames optimally; and the Rapid Key Frame Extraction Algorithm, which employs clustering techniques to identify typical key frames. However, a clear prerequisite remains for a more versatile KFE technique that can address generic applications rather than specific use cases. Evolutionary algorithms offer a powerful solution for achieving optimal KFE. This proposed method leverages an interactive GA with a well-designed Fitness Function and elitism-based survivor selection to enhance performance. This proposed algorithm has been tested on diverse datasets, including VSUMM, SumMe, Mall, user-generated videos, surveillance footage from Amrita Vishwa Vidyapeetham University (Coimbatore, India), and web-sourced videos. The results demonstrate that the proposed KFE approach adheres to benchmark data and captures additional significant frames. Compared to Differential Evolution (DE) techniques and Deep Learning (DL) models from the literature, this recommended algorithm demonstrates superior efficiency, as verified through quantitative and qualitative evaluation metrics. Furthermore, the computational complexity of the GA is intricately compared to that of DE and DL-based approaches, highlighting the distinct efficiencies and performance features.
随着技术的飞速发展，视频以多种格式被捕获、存储和共享，这增加了对总结技术的需求，以便能够缩短观看时长。关键帧提取（KFE）算法在视频总结、压缩和离线分析中至关重要。本研究旨在为通用视频开发一种高效的KFE方法。现有方法包括自适应关键帧提取算法，该算法在确保最大内容覆盖的同时减少冗余；最优关键帧提取算法，该算法利用遗传算法（GA）来优化选择关键帧；以及快速关键帧提取算法，该算法采用聚类技术来识别典型关键帧。然而，对于一种能够处理通用应用而非特定用例的更通用的KFE技术，仍然存在明确的前提条件。进化算法为实现最优KFE提供了一个强大的解决方案。所提出的方法利用具有精心设计的适应度函数和基于精英主义的幸存者选择的交互式GA来提高性能。所提出的算法已经在各种数据集上进行了测试，包括VSUMM、SumMe、Mall、用户生成的视频、印度阿姆瑞塔维什瓦维迪亚佩特姆大学（哥印拜陀）的监控录像以及网络来源的视频。结果表明，所提出的KFE方法符合基准数据并捕获了额外的重要帧。与文献中的差分进化（DE）技术和深度学习（DL）模型相比，通过定量和定性评估指标验证，该推荐算法具有更高的效率。此外，还将GA的计算复杂度与基于DE和DL的方法进行了复杂的比较，突出了它们不同的效率和性能特点。
