Unsupervised Video Skimming with Adaptive Hierarchical Shot Detection

Video skimming involves generating a concise representation that captures all its significant information. However, conventional skimming techniques often fail to capture different shots in a video due to their inability to detect scene modifications and incorporate the hierarchical structure of video content. This work proposes an unsupervised hierarchical method for video skimming, called Hierarchical Time-aware Skimming - HieTaSkim, in which video content is modeled as a graph, and an adaptive strategy is employed to produce hierarchical graph cuts. Those cuts are used to identify the most relevant video segments or keyshots, allowing the extraction of frames' sequences that convey the video's central message and resulting in a more effective and accurate video summary. Experimental results demonstrate that the proposed approach outperforms other state-of-the-art unsupervised methods for video skimming, achieving in the SumMe dataset an F-score of 39.9 which represents an improvement of 10% at least.