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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.
Rajan et al. (Thu,) studied this question.