Keyframe Extraction for Low-Motion Video Summarization Using K-Means Clustering

The rate of increase in multimedia data required the need for an improved bandwidth utilization and storage capacity. However, low-motion videos come with a large number feature-related frames due to its static background. These redundant result difficulty terms video streaming, retrieval, transmission. In other improve user experience, summarization technologies were proposed. techniques presented select representative from full-length remove duplicated ones. Though, improvement was recorded keyframe extraction process. observed be extracted as keyframes. Therefore, this study presents scheme summarization. proposed utilizes k-means clustering approach group within given into clusters. Furthermore, frame each cluster keyframe. results obtained shown that outperforms existing compression ratio, precision recall rates value 26.62%, 13.78%, 6.63% respectively