On multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: an automated anti-pornography system
The main objective on this study proposed anti-pornography system works on four machine learning methods in two different stages namely skin detector stage and pornography classifier stage. A multi-agent learning is used twice. In the first stage, we propose a multi-agent learning method that combin...
| Main Authors: | , , , , , |
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| Format: | Article |
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Elsevier
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/34731/ |
| _version_ | 1848847855756771328 |
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| author | Zaidan, Aws Alaa Ahmad, Nurul Nadia Abdul Karim, Hezerul Larbani, Moussa Zaidan, Bilal Bahaa Sali, Aduwati |
| author_facet | Zaidan, Aws Alaa Ahmad, Nurul Nadia Abdul Karim, Hezerul Larbani, Moussa Zaidan, Bilal Bahaa Sali, Aduwati |
| author_sort | Zaidan, Aws Alaa |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The main objective on this study proposed anti-pornography system works on four machine learning methods in two different stages namely skin detector stage and pornography classifier stage. A multi-agent learning is used twice. In the first stage, we propose a multi-agent learning method that combines the Bayesian method with a grouping histogram (GH) technique and the back-propagation neural network with a segment adjacent-nested (SAN) technique based on the YCbCr and RGB colour spaces respectively, to extract skin regions from the image accurately with taking into consideration the problems of the light-changing conditions, skin-like colour and reflection from glass and water. In the second stage, the features from the skin are extracted to classify the images into either pornographic or non-pornographic. Inaccurate classification occurs when different image sizes are used in the existing anti-pornography systems. Thus, this paper proposes a multi-agent learning that combines the Bayesian method with a grouping histogram technique again to extract the features from the skin detection based on YCbCr colour space and the back propagation neural network method using shape features extracted again from skin detection. The classification of the pornographic images becomes more robust to the variation in images sizes. The findings from this study have shown that the proposed multi-agent learning system for skin detection has produced a significant rate of true positives (TP) (i.e., 98.44%). In addition, it has achieved a significant low average rate for the false positives (FP) (i.e., only 0.14%) while the proposed multi-agent learning for pornography classifier has produced significant rates of TP (i.e., 96%). Moreover, it has achieved a significant low average rate of FP (i.e., only 2.67%). The experimental results show that multi-agent learning in the skin detector and pornography classifier are more efficient than other approaches. |
| first_indexed | 2025-11-15T09:25:14Z |
| format | Article |
| id | upm-34731 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:25:14Z |
| publishDate | 2014 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-347312015-12-21T12:46:35Z http://psasir.upm.edu.my/id/eprint/34731/ On multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: an automated anti-pornography system Zaidan, Aws Alaa Ahmad, Nurul Nadia Abdul Karim, Hezerul Larbani, Moussa Zaidan, Bilal Bahaa Sali, Aduwati The main objective on this study proposed anti-pornography system works on four machine learning methods in two different stages namely skin detector stage and pornography classifier stage. A multi-agent learning is used twice. In the first stage, we propose a multi-agent learning method that combines the Bayesian method with a grouping histogram (GH) technique and the back-propagation neural network with a segment adjacent-nested (SAN) technique based on the YCbCr and RGB colour spaces respectively, to extract skin regions from the image accurately with taking into consideration the problems of the light-changing conditions, skin-like colour and reflection from glass and water. In the second stage, the features from the skin are extracted to classify the images into either pornographic or non-pornographic. Inaccurate classification occurs when different image sizes are used in the existing anti-pornography systems. Thus, this paper proposes a multi-agent learning that combines the Bayesian method with a grouping histogram technique again to extract the features from the skin detection based on YCbCr colour space and the back propagation neural network method using shape features extracted again from skin detection. The classification of the pornographic images becomes more robust to the variation in images sizes. The findings from this study have shown that the proposed multi-agent learning system for skin detection has produced a significant rate of true positives (TP) (i.e., 98.44%). In addition, it has achieved a significant low average rate for the false positives (FP) (i.e., only 0.14%) while the proposed multi-agent learning for pornography classifier has produced significant rates of TP (i.e., 96%). Moreover, it has achieved a significant low average rate of FP (i.e., only 2.67%). The experimental results show that multi-agent learning in the skin detector and pornography classifier are more efficient than other approaches. Elsevier 2014-05-05 Article PeerReviewed Zaidan, Aws Alaa and Ahmad, Nurul Nadia and Abdul Karim, Hezerul and Larbani, Moussa and Zaidan, Bilal Bahaa and Sali, Aduwati (2014) On multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: an automated anti-pornography system. Neurocomputing, 131. pp. 397-418. ISSN 0925-2312; ESSN: 1872-8286 http://www.sciencedirect.com/science/article/pii/S0925231213009326 10.1016/j.neucom.2013.10.003 |
| spellingShingle | Zaidan, Aws Alaa Ahmad, Nurul Nadia Abdul Karim, Hezerul Larbani, Moussa Zaidan, Bilal Bahaa Sali, Aduwati On multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: an automated anti-pornography system |
| title | On multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: an automated anti-pornography system |
| title_full | On multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: an automated anti-pornography system |
| title_fullStr | On multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: an automated anti-pornography system |
| title_full_unstemmed | On multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: an automated anti-pornography system |
| title_short | On multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: an automated anti-pornography system |
| title_sort | on multi-agent learning neural and bayesian methods in skin detector and pornography classifier: an automated anti-pornography system |
| url | http://psasir.upm.edu.my/id/eprint/34731/ http://psasir.upm.edu.my/id/eprint/34731/ http://psasir.upm.edu.my/id/eprint/34731/ |