Violent Scenes Detection Using Mid-Level Violence Clustering
Shinichi Goto, Terumasa Aoki · 2014
This work proposes a novel system for Violent Scenes Detection, which is based on the combination of visual and audio features with machine learning at segment-level.Multiple Kernel Learning is applied so that multimodality of videos can be maximized.In particular, Mid-level Violence Clustering is proposed in order for mid-level concepts to be implicitly learned, without using manually tagged annotations.Finally a violence-score for each shot is calculated.The whole system is trained ona dataset from MediaEval 2013 Affect Task and evaluated by its official metric.The obtained results outperformed its best score.