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Pornprobe: an LDA-SVM based pornography detection system

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International Multimedia Conference archive
Proceedings of the seventeen ACM international conference on Multimedia table of contents

Beijing, China

DEMONSTRATION SESSION: Technical demonstrations session 2 table of contents

Pages 1003-1004

Year of Publication: 2009

ISBN:978-1-60558-608-3

Authors

Sheng Tang	Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Jintao Li	Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Yongdong Zhang	Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Cheng Xie	Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Ming Li	Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Yizhi Liu	Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Xiufeng Hua	Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Yan-Tao Zheng	School of Computing, National University of Singapore, Singapore, Singapore
Jinhui Tang	School of Computing, National University of Singapore, Singapore, Singapore
Tat-Seng Chua	School of Computing, National University of Singapore, Singapore, Singapore

Sponsor

SIGMULTIMEDIA: ACM Special Interest Group on Multimedia

Publisher

ACM New York, NY, USA

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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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	H. Zheng; "Maximum entropy modeling for skin detection: with an application to Internet filtering"; Ph.D. Thesis, Univeristé des Sciences et Technologies de Lille, France, 2004.
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	3	Blei,D.M., Ng, A.Y., and Jordan, M.J; "Latent Dirichlet allocation". Journal of Machine Learning Research, 3, 2003, 993--1022.
	4	S. Tang, J.-T. Li, M. Li, C. Xie, Y.-Z. Liu, K. Tao, S.-X. Xu; "TRECVID 2008 High-Level Feature Extraction By MCG-ICT-CAS"; Proc. TRECVID Workshop, Gaithesburg, USA, Nov 2008.