K-means Clustering Algorithm

K-means clustering is a classic unsupervised learning algorithm widely used in data grouping and pattern recognition applications. This algorithm iteratively partitions data points into K clusters, ensuring high similarity within each cluster while maximizing dissimilarity between different clusters. In video processing, K-means can be employed for keyframe extraction by clustering video frame features to identify representative frames.

The core algorithmic steps include: Randomly initializing K cluster centers Calculating the distance from each data point to all centers and assigning it to the nearest cluster Recalculating cluster centroids as new centers Repeating steps 2-3 until convergence criteria are met

MATLAB implementation can leverage built-in functions like kmeans() for optimized computational efficiency. Critical considerations include feature selection (e.g., color histograms, optical flow features for video frames) and distance metrics (commonly Euclidean distance). The algorithm is sensitive to initial center positions, which can be mitigated through multiple random initializations with optimal result selection. The choice of K can be evaluated using methods like the elbow method or silhouette coefficients.

The algorithm's advantage in keyframe extraction lies in its ability to automatically discover natural groupings in video content, though it requires careful feature engineering and parameter tuning. MATLAB's matrix operation capabilities and visualization tools provide significant advantages for algorithm debugging and result analysis, including functions like pca() for dimensionality reduction and scatter3() for 3D cluster visualization.