Pornprobe: an LDA-SVM based pornography detection system

  • Authors:

  • Sheng Tang;Jintao Li;Yongdong Zhang;Cheng Xie;Ming Li;Yizhi Liu;Xiufeng Hua;Yan-Tao Zheng;Jinhui Tang;Tat-Seng Chua

  • Affiliations:

  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;School of Computing, National University of Singapore, Singapore, Singapore;School of Computing, National University of Singapore, Singapore, Singapore;School of Computing, National University of Singapore, Singapore, Singapore

  • Venue:
  • MM '09 Proceedings of the 17th ACM international conference on Multimedia
  • Year:
  • 2009

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Abstract

We present PornProbe, a pornography detection system that detects pornographic contents in videos. To build such a detection system, we leverage a large scale training data set with 65,827 positive training image samples out of a total of 420,615 training samples, and a novel detection scheme based on hierarchical LDA-SVM. The system combines the unsupervised clustering in Latent Dirichlet Allocation (LDA) and supervised learning in Support Vector Machine, so as to achieve both high precision and recall while ensuring efficiency in both training and testing. This demonstration shows how the system detects the pornographic scenes in restricted artistic (RA) movies.