Multi-classifier scheme with low-level visual feature for adult image classification
As the usage and accessing of children to the web resources with porn images contain is growing, requirement of methods with high accuracy to detect and block adult images is a necessity. In this paper, a novel multi-classifier scheme is proposed based on low-level feature to exploit of advantages i...
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my.utm.294752017-02-04T07:26:32Z http://eprints.utm.my/id/eprint/29475/ Multi-classifier scheme with low-level visual feature for adult image classification Bozorgi, M. Maarof, Mohd. Aizaini Sam, L. Z. QA75 Electronic computers. Computer science As the usage and accessing of children to the web resources with porn images contain is growing, requirement of methods with high accuracy to detect and block adult images is a necessity. In this paper, a novel multi-classifier scheme is proposed based on low-level feature to exploit of advantages in classifier ensemble for achieving better accuracy compared to single classifier that applied to adult images detection. Low-level features are three different MPEG-7 descriptors include Color Layout Descriptor (CLD), Scalable Color Descriptor (SCD) and Edge Histogram Descriptor (EHD). In the classification part Support Vector Machine (SVM) and AdaBoost are applied and combined. Experimental results indicate that proposed scheme works better than each single classifier that used in the experiments. Springer-Verlag GmbH Berlin Heidelberg 2011 Book Section PeerReviewed Bozorgi, M. and Maarof, Mohd. Aizaini and Sam, L. Z. (2011) Multi-classifier scheme with low-level visual feature for adult image classification. In: Software Engineering and Computer Systems: Second International Conference, ICSECS 2011, Kuantan, Pahang, Malaysia, June 27-29, 2011, Proceedings, Part III. Communications in Computer and Information Science, 181 . Springer-Verlag GmbH Berlin Heidelberg, Netherlands, pp. 793-802. ISBN 978-364222202-3 http://dx.doi.org/10.1007/978-3-642-22203-0_66 10.1007/978-3-642-22203-0_66 |
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QA75 Electronic computers. Computer science Bozorgi, M. Maarof, Mohd. Aizaini Sam, L. Z. Multi-classifier scheme with low-level visual feature for adult image classification |
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As the usage and accessing of children to the web resources with porn images contain is growing, requirement of methods with high accuracy to detect and block adult images is a necessity. In this paper, a novel multi-classifier scheme is proposed based on low-level feature to exploit of advantages in classifier ensemble for achieving better accuracy compared to single classifier that applied to adult images detection. Low-level features are three different MPEG-7 descriptors include Color Layout Descriptor (CLD), Scalable Color Descriptor (SCD) and Edge Histogram Descriptor (EHD). In the classification part Support Vector Machine (SVM) and AdaBoost are applied and combined. Experimental results indicate that proposed scheme works better than each single classifier that used in the experiments. |
format |
Book Section |
author |
Bozorgi, M. Maarof, Mohd. Aizaini Sam, L. Z. |
author_facet |
Bozorgi, M. Maarof, Mohd. Aizaini Sam, L. Z. |
author_sort |
Bozorgi, M. |
title |
Multi-classifier scheme with low-level visual feature for adult image classification |
title_short |
Multi-classifier scheme with low-level visual feature for adult image classification |
title_full |
Multi-classifier scheme with low-level visual feature for adult image classification |
title_fullStr |
Multi-classifier scheme with low-level visual feature for adult image classification |
title_full_unstemmed |
Multi-classifier scheme with low-level visual feature for adult image classification |
title_sort |
multi-classifier scheme with low-level visual feature for adult image classification |
publisher |
Springer-Verlag GmbH Berlin Heidelberg |
publishDate |
2011 |
url |
http://eprints.utm.my/id/eprint/29475/ http://dx.doi.org/10.1007/978-3-642-22203-0_66 |
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1643648306539134976 |
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13.160551 |