Context-aware horror video scene recognition via cost-sensitive sparse coding
Along with the ever-growing Web, horror video sharing through the Internet has affected our children's psychological health. Most of current horror video filtering researches pay more attention to the extraction of global features or selection of an optimal classifier, while neglecting the underlying contexts in a scene. In this paper, a novel cost-sensitive sparse coding (CSC) model is proposed to address the context inside scene and interrelations between audio-visual features simultaneously. The model essentially includes two aspects: one is to construct inner contextual structure among frames from same scene based on a ∊-graph; the other one is to extend the classic sparse coding technique into a cost-sensitive sparse coding model for graph pattern classification as well as audio-visual features fusion through graph kernel. The experiments on various video scenes demonstrate that our method's performance is superior to the other existing algorithm.