Technical, Business, and Legal Dimensions of Protecting Children from Pornography on the Internet Proceedings of a Workshop (2002) / Chapter Skim
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3 Categorization of Images
3 Categorization of Images
Pages 11-15
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From page 11... ...
We can tell sort of whether a picture has nearly naked people in it. But there is no program that reliably determines whether there are people wearing clothing in a picture.
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From page 12... ...
The security industry usually says that people faced with many false positives get bored and do not want to deal with the problem. On the other hand, a high rate of false negatives is not a concern in this context.
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From page 13... ...
This program is slightly better at identifying pictures of puddings than it is at detecting pictures of naked people, because an apple tart looks like skin arranged in lines and strips. Generally, if a Web page contains pictures of puddings, then the program says each picture is a problem and, therefore, the Web page is a problem.
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From page 14... ...
To mark about 90 percent of the pornographic pictures, you would get about 8 percent false positives, which might be a very serious issue. Unless you are in the business of finding out who is looking at rude pictures, then 8 percent false alarms would be completely unacceptable.
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From page 15... ...
Something like this could be regarded as a final course project in information-retrieval computer vision for a statistical English program. This will remain true for the foreseeable future.
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