Video Frame Dimensionality Reduction Using LLE Algorithm with Keyframe Extraction via FCM Clustering

In this technical documentation, we describe a comprehensive approach for video frame analysis using dimensionality reduction and clustering techniques. The implementation begins with applying the Locally Linear Embedding (LLE) algorithm to project high-dimensional video frame data into a lower-dimensional space. LLE operates by preserving local neighborhood relationships between data points, making it particularly suitable for video frames where temporal and spatial coherence matters. The algorithm implementation typically involves calculating neighborhood weights matrix and solving eigenvalue problems to obtain optimal low-dimensional embeddings.

Following dimensionality reduction, we employ the Fuzzy C-Means (FCM) clustering algorithm for keyframe extraction in video summarization. FCM assigns membership values to each frame across different clusters, allowing soft classification based on similarity measures in the reduced feature space. The algorithm iteratively updates cluster centroids and membership degrees until convergence, effectively grouping visually similar frames. This process enables automatic identification of representative keyframes that capture essential video content while eliminating redundant frames.

Through this integrated processing pipeline, we generate detailed and meaningful video summaries that facilitate efficient video content analysis. The implementation typically requires parameter tuning for neighborhood size in LLE and cluster count in FCM to optimize results for different video types and durations.