Multi-view learning for visual violence recognition with maximum entropy discrimination and deep features

Abstract

Harmful information full of violence, whose transmission is difficult to control, is one of the negative effects that the Internet brings to us today. Such corrosive information in the network environment infiltrates the mental health of adolescents. Many young people who lack judgment tend to imitate violent behaviors that are propagated on the network. This study applies multi-view learning to violent behavior recognition of still images, and can be useful for the research of network image or video information monitoring and filtering. In this paper, to start with, we construct a violence image recognition database, which contains 5974 violence images and 11,516 non-violence images. From this database, different datasets can be constructed for model and algorithm evaluations. Then we propose a multi-view maximum entropy discriminant (MVMED+) model for learning with different numbers of views. It can combine various features of the images and thus classify the images with the complementary information between the views. For efficient optimization, we further derive a sequential minimal optimization algorithm to train the model. In the experiments, we use handcrafted features and deep learning features to validate the effectiveness of the proposed model, respectively. As a useful byproduct, large performance improvements on visual violence recognition are further observed with deep learning features.