Keyframe Extraction Using K-means Method

This paper describes a keyframe extraction method based on the K-means algorithm, which involves selecting appropriate K-values to identify representative frames. The primary objective of keyframe extraction is to identify the most representative and informative frames from video streams, thereby facilitating better understanding and analysis of video content. By implementing the K-means algorithm, frames can be clustered according to their visual similarity, with representative frames from each cluster selected as keyframes. This approach typically involves calculating frame features using techniques like color histograms or deep learning features, then applying K-means clustering to group similar frames. The implementation commonly uses distance metrics such as Euclidean distance to measure frame similarity and centroid calculation to identify cluster representatives. This method significantly reduces video data volume while maintaining essential content, thereby improving the efficiency of video processing and analysis workflows. Key parameters include determining the optimal K-value through methods like elbow criterion or silhouette analysis, and setting appropriate convergence thresholds for the clustering algorithm.