Bloody Image Classification with Global and Local Features

摘要：

　　Object content understanding in images and videos draws more and more attention nowadays.However,only few existing methods have addressed the problem of bloody scene detection in images.Along with the widespread popularity of the Internet,violent contents have affected our daily life.In this paper,we propose region-based techniques to identify a color image being bloody or not.Firstly,we have established a new dataset containing 25431 bloody images and 25431 nonbloody images.These annotated images are derived from the Violent Scenes Dataset,a public shared dataset for violent scenes detection in Hollywood movies and web videos.Secondly,we design a bloody image classification method with global visual features using Support Vector Machines.Thirdly,we also construct a novel bloody region identification approach using Convolutional Neural Networks.Finally,comparative experiments show that bloody image classification with local features is more effective.

关键词： Bloody image classification Violent scenes dataset Support vector machines Convolutional Neural Networks

作者: Song-Lu Chen Chun Yang Chao Zhu Xu-Cheng Yin

作者单位: School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing,China

会议类型: 国际会议

会议名称: 第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)

会议地点: 成都

会议语种:英文

页码: 379-391

在线出版日期: 2016-11-03（万方平台首次上网日期，不代表论文的发表时间）