Abstract
We extend earlier work on detecting pornographic images. Our focus is on the classification stage and we give new results for a variety of classical and modern classifiers. We find the artificial neural network offers a statistically significant improvement. In all cases the error rate is too high unless deployed sensitively so we show how such a system may be built into a commercial environment.
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Bosson, A., Cawley, G.C., Chan, Y., Harvey, R. (2002). Non-retrieval: Blocking Pornographic Images. In: Lew, M.S., Sebe, N., Eakins, J.P. (eds) Image and Video Retrieval. CIVR 2002. Lecture Notes in Computer Science, vol 2383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45479-9_6
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DOI: https://doi.org/10.1007/3-540-45479-9_6
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