Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning

This study proposed a pornography classifier using multi-agent learning as a combination of the Bayesian method using color features extracted from skin detection based on the YCbCr color space and the back-propagation neural network method using shape features also extracted from skin detection. The classification of pornographic images was made more robust to the variation of images despite size engineering problems. Previous studies failed to achieve such robustness. Findings showed that the proposed multi-agent learning-based pornography classifier has produced significant TP and TN average rates (i.e., 96% and 97.33%, respectively). In addition, the proposed classifier has achieved a significantly low average rate of FN and FP (i.e., only 4% and 2.67%, respectively). The implementation of this algorithm is crucial and significant not only in identifying pornography but also in blocking Web sites that covertly promote pornography.