Seems like we were having this same problem with email spam, and Bayesian-based learning filters revolutionized the spam filtering landscape. Has anyone tried t

Seems like we were having this same problem with email spam, and Bayesian-based learning filters revolutionized the spam filtering landscape. Has anyone tried throwing computer learning at this problem? We as humans can readily classify images into three vague categories: clean, questionable, and pornographic. The problem of classification is not only one of determining which bucket an image falls into but also one of determining where the boundaries between buckets are. Is a topless woman pornographic? A topless man? A painting of a topless woman created centuries ago by a well-recognized artist? A painting of a topless woman done yesterday by a relatively unknown artist? An infant being bathed? A woman breastfeeding her baby? Reasonable people may disagree on which bucket these examples fall in. So what if I create three filter sets: restrictive, moderate, and permissive, and then categorize 1,000 sample images as one of those three categories for each filter set (restrictive could be equal to moderate but filter questionable images as well as pornographic ones). Assuming that the learning algorithm was programmed to look at a sufficiently large number of image attributes, this approach should easily be capable of creating the most robust (and learning!) filter to date. Has anyone done this?