Using Multiple Classifiers to Improve Accuracy

Using Multiple Classifiers to Improve Accuracy

Our earliest algorithms were quite primitive compared to current trends.  To improve accuracy, we applied several classifiers to detect nudity, including an ensemble of skin based classifiers, pattern detection, skin blob detection and feature detection.

With the recent advances in deep learning, the use of some of the past techniques have become ineffective.  One could still combine the output of deep learning to build an ensemble based classifier, however you still have to deal with the same complexities of combining multiple predictions, assigning weights among others to deliver improvements in accuracies. 

Within vRate, applying multiple methods, which have been tuned over time, have helped us deliver automated nudity detection which is both effective and economical.  In upcoming posts, we hope to share our journey in technology right from the early days in 2009.

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