Semantic principal video shot classification via mixture Gaussian

As digital cameras become more affordable, digital video now plays an important role in medical education and healthcare. In this paper, we propose a novel framework to facilitate semantic classification of surgery education videos. Specifically, the framework includes: (a) semantic-sensitive video content characterization via principal video shots, (b) semantic video classification via a mixture Gaussian model to bridge the semantic gap between low-level visual features and semantic visual concepts in a specific surgery education video domain.