Abstract
We extend earlier work on detecting pornographic images.Our focus is on the classification stage and we give new results for avariety of classical and modern classifiers. We find the artificial neuralnetwork offers a statistically significant improvement. In all cases theerror rate is too high unless deployed sensitively so we show how such a system may be built into a commercial environment.
Cite
CITATION STYLE
Bosson, A., Cawley, G. C., Chan, Y., & Harvey, R. (2002). Non-retrieval: Blocking pornographic images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2383, pp. 50–60). Springer Verlag. https://doi.org/10.1007/3-540-45479-9_6
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