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 ϵ-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.