Maximum relevant diversity aware, multi-video summarization using clustering and evolutionally multi-objective optimization

With the exponential increase in video data, efficient video summarization techniques are crucial for handling and analyzing large-scale collections. Multi-video summarization poses unique challenges due to inter-video correlations, redundancy, and variability in content. This paper introduces a novel multi-video summarization approach that combines clustering with evolutionary multi-objective optimization to produce concise, diverse, and informative summaries. Our method begins by segmenting videos into scenes and representing each using I3D CNN features for spatial and temporal dynamics. Through clustering, similar segments across videos are grouped to maximize content diversity while minimizing redundancy. Key video summarization features-visual attention, coverage, and diversity-are then extracted from clusters and segments, and balanced using a multi-objective optimization algorithm. The final summary is generated by selecting a subset of optimized solutions based on a predefined summary ratio, ensuring relevance, coverage, and diversity. Extensive experiments on the Tour20 dataset demonstrate the superiority of our approach, highlighting its potential to address the complexities of multi-video summarization.