Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning
This study proposed a pornography classifier using multi-agent learning as a combination of the Bayesian method using color features extracted from skin detection based on the YCbCr color space and the back-propagation neural network method using shape features also extracted from skin detection. Th...
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my.um.eprints.193122018-09-20T03:10:49Z http://eprints.um.edu.my/19312/ Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning Zaidan, A.A. Karim, H.A. Ahmad, N.N. Zaidan, B.B. Mat Kiah, M.L. QA75 Electronic computers. Computer science This study proposed a pornography classifier using multi-agent learning as a combination of the Bayesian method using color features extracted from skin detection based on the YCbCr color space and the back-propagation neural network method using shape features also extracted from skin detection. The classification of pornographic images was made more robust to the variation of images despite size engineering problems. Previous studies failed to achieve such robustness. Findings showed that the proposed multi-agent learning-based pornography classifier has produced significant TP and TN average rates (i.e., 96% and 97.33%, respectively). In addition, the proposed classifier has achieved a significantly low average rate of FN and FP (i.e., only 4% and 2.67%, respectively). The implementation of this algorithm is crucial and significant not only in identifying pornography but also in blocking Web sites that covertly promote pornography. World Scientific Publishing 2015 Article PeerReviewed Zaidan, A.A. and Karim, H.A. and Ahmad, N.N. and Zaidan, B.B. and Mat Kiah, M.L. (2015) Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning. Journal of Circuits, Systems and Computers, 24 (02). p. 1550023. ISSN 0218-1266 http://dx.doi.org/10.1142/S0218126615500231 doi:10.1142/S0218126615500231 |
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QA75 Electronic computers. Computer science Zaidan, A.A. Karim, H.A. Ahmad, N.N. Zaidan, B.B. Mat Kiah, M.L. Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning |
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This study proposed a pornography classifier using multi-agent learning as a combination of the Bayesian method using color features extracted from skin detection based on the YCbCr color space and the back-propagation neural network method using shape features also extracted from skin detection. The classification of pornographic images was made more robust to the variation of images despite size engineering problems. Previous studies failed to achieve such robustness. Findings showed that the proposed multi-agent learning-based pornography classifier has produced significant TP and TN average rates (i.e., 96% and 97.33%, respectively). In addition, the proposed classifier has achieved a significantly low average rate of FN and FP (i.e., only 4% and 2.67%, respectively). The implementation of this algorithm is crucial and significant not only in identifying pornography but also in blocking Web sites that covertly promote pornography. |
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Article |
author |
Zaidan, A.A. Karim, H.A. Ahmad, N.N. Zaidan, B.B. Mat Kiah, M.L. |
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Zaidan, A.A. Karim, H.A. Ahmad, N.N. Zaidan, B.B. Mat Kiah, M.L. |
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Zaidan, A.A. |
title |
Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning |
title_short |
Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning |
title_full |
Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning |
title_fullStr |
Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning |
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Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning |
title_sort |
robust pornography classification solving the image size variation problem based on multi-agent learning |
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World Scientific Publishing |
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2015 |
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http://eprints.um.edu.my/19312/ http://dx.doi.org/10.1142/S0218126615500231 |
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