Image-based pornography detection
This paper presents an algorithm for detecting pornography or nudity in color images based on a skin color distribution model. The skin color model is based on correlation analyses of RGB and HSV color components. This model is used to identify and locate skin regions in an image. The skin regions a...
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2008
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oai:animorepository.dlsu.edu.ph:faculty_research-15672022-08-24T16:16:34Z Image-based pornography detection Ap-Apid, Rigan P. This paper presents an algorithm for detecting pornography or nudity in color images based on a skin color distribution model. The skin color model is based on correlation analyses of RGB and HSV color components. This model is used to identify and locate skin regions in an image. The skin regions are analyzed for clues indicating nudity or non-nudity such as their sizes and relative distances from each other. The Nudity Detection Algorithm is able to detect nudity with a 94.85% recall and a false positive rate of 6.52% on a set of images consisting of 641 nude images and 936 non-nude images. 2008-12-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/568 Faculty Research Work Animo Repository Nudity Pornography Imaging systems Color vision Human skin color Computer Sciences |
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De La Salle University |
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De La Salle University Library |
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Asia |
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Philippines Philippines |
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De La Salle University Library |
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DLSU Institutional Repository |
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Nudity Pornography Imaging systems Color vision Human skin color Computer Sciences |
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Nudity Pornography Imaging systems Color vision Human skin color Computer Sciences Ap-Apid, Rigan P. Image-based pornography detection |
| description |
This paper presents an algorithm for detecting pornography or nudity in color images based on a skin color distribution model. The skin color model is based on correlation analyses of RGB and HSV color components. This model is used to identify and locate skin regions in an image. The skin regions are analyzed for clues indicating nudity or non-nudity such as their sizes and relative distances from each other. The Nudity Detection Algorithm is able to detect nudity with a 94.85% recall and a false positive rate of 6.52% on a set of images consisting of 641 nude images and 936 non-nude images. |
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text |
| author |
Ap-Apid, Rigan P. |
| author_facet |
Ap-Apid, Rigan P. |
| author_sort |
Ap-Apid, Rigan P. |
| title |
Image-based pornography detection |
| title_short |
Image-based pornography detection |
| title_full |
Image-based pornography detection |
| title_fullStr |
Image-based pornography detection |
| title_full_unstemmed |
Image-based pornography detection |
| title_sort |
image-based pornography detection |
| publisher |
Animo Repository |
| publishDate |
2008 |
| url |
https://animorepository.dlsu.edu.ph/faculty_research/568 |
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1743177757263659008 |