Graph-based centrality framework for effective multi-video summarization

The exponential growth of video content presents substantial challenges in summarizing and retrieving relevant information, particularly in multi-video scenarios involving heterogeneous sources. This paper presents an unsupervised, graph-based centrality framework for multi-video summarization. Segment representations are extracted using 3D Convolutional Neural Networks (3DCNNs) to capture both spatial and temporal features. We introduce three novel ranking algorithms — Weighted Degree Centrality (WDC), V-Rank, and VL-Rank — extending classical methods such as Degree Centrality, PageRank, and LexRank. These algorithms incorporate visual saliency, motion, and semantic similarity to ensure relevance, diversity, and structural representativeness. The framework comprises four stages: segmentation, graph construction, ranking, and selection. We provide a detailed computational analysis, including time complexity and convergence behavior. VL-Rank achieves significantly faster convergence than PageRank through a normalized propagation scheme, while WDC offers a highly efficient, non-iterative alternative. Evaluations on the Tour20 dataset demonstrate that the proposed methods outperform state-of-the-art approaches, with WDC achieving a mean F1 score of 0.741 compared to 0.680 for the Multi-Stream baseline. The framework is both effective and scalable, making it suitable for large-scale or real-time applications.