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About this paper
Vasilii Korolkov
An adaptive, unsupervised framework for scene segmentation and keyframe extraction efficiently processes diverse video content using visual cues. The system achieves over 92% keyframe coverage and dynamically adjusts segmentation granularity based on video duration, operating effectively on commodity hardware.
Key insights
- An adaptive policy map, which chooses segmentation strategies based on video duration, allows for content-sensitive and generalizable structural decomposition without prior knowledge or extensive training.
- Combining low-level perceptual cues (brightness, sharpness) provides a computationally efficient and transparent method for selecting representative keyframes, avoiding learned embeddings.
- Robust structural segmentation and keyframe extraction can be achieved through unsupervised, visual-only heuristics, effectively bridging the gap between simple detectors and complex deep learning models for production environments.
Summary by alphaXiv.
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