Please use this identifier to cite or link to this item: https://doi.org/10.1145/1631272.1631490
Title: PornProbe: An LDA-SVM based pornography detection system
Authors: Tang, S. 
Li, J.
Zhang, Y.
Xie, C.
Li, M.
Liu, Y.
Hua, X.
Zheng, Y.-T.
Tang, J. 
Chua, T.-S. 
Keywords: Latent dirichlet allocation
Pornography detection
SVM
Issue Date: 2009
Source: Tang, S.,Li, J.,Zhang, Y.,Xie, C.,Li, M.,Liu, Y.,Hua, X.,Zheng, Y.-T.,Tang, J.,Chua, T.-S. (2009). PornProbe: An LDA-SVM based pornography detection system. MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums : 1003-1004. ScholarBank@NUS Repository. https://doi.org/10.1145/1631272.1631490
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.
Source Title: MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
URI: http://scholarbank.nus.edu.sg/handle/10635/39938
ISBN: 9781605586083
DOI: 10.1145/1631272.1631490
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