An Empirical Taxonomy of Video Summarization Model From a Statistical Perspective

This paper has thrown out a comprehensive taxonomic view of all different video summarization techniques that show variously adopted methodologies efficiently condensing large video data. The review categorizes and evaluates techniques against their core approaches: clustering-based methods, deep learning frameworks, and multimodal integration strategies. Some of the notable techniques include the SVS_MCO method using DBSCAN clustering optimized by the Artificial Algae Algorithm and the KDAN framework, which leverages knowledge distillation for supervised summarization. The review also gives advanced models like the Query-based Deep African Vulture Learning and the Audio-Visual Recurrent Network, which are observed to be very helpful in handling dynamic and multimodal video summarization tasks. Performance of such techniques benchmarks against datasets like SumMe, TVSum, and other custom datasets and has shown considerable improvement in precision, recall, and F1-scores. In the final analysis, this paper identifies some of the best models that apply in domains such as multimedia, surveillance, and user-generated content and finally discusses improvements in adaptability and efficiency of video summarization technologies in the future. Results show a trend that reinforcement learning combined with attention mechanisms and self-supervised learning will provide robust, scalable summarization solutions.