Along with the ever-growing Web

horror videos sharing in the Internet has threatened children's psychological health.It is necessary to effectively recognize and filter out these horror videos.So far

several horror video recognition methods based on Multi-Instance Learning (MIL) have been proposed.However

all these methods suppose that the instances in a bag are independent

ignoring the contextual cue and statistical cue in horror videos.In this paper

we propose a novel multi-view joint sparse coding model for horror video recognition.This model considers video from three different viewpoints including set view

contextual view and statistical view.The set view treats a video as a set of independent frames.The context view models the contextual relationship among key frames in a video using an e-graph.The statistical view represents a video as a histogram feature based on bag-of-words model.Then

three kernel functions are designed for the three viewpoints

respectively.Finally

the three kernels are integrated into a unified multi-view joint sparse coding classification framework to recognize the horror video scenes based on reconstruction residual.Experiments on a horror video dataset demonstrate that our method's performance is superior to the other existing algorithms.