EFUI: An ensemble framework using uncertain inference for pornographic image recognition

Pornographic image recognition is a challenging task due to the subjective definition and complex contextual information. In this paper, we propose an ensemble framework using uncertain inference (EFUI) for pornographic image recognition. The EFUI employs bayesian network (BN) as uncertain inference engine, while prior global confidence and uncertain evidence of local semantic components are acquired by deep learning networks. Specifically, we construct the graphical model of BN based on the internal contextual relationship of local semantic components, which conforms to common sense. The prior global confidence of pornography for candidate image is extracted using GoogleNet/ResNet-50. We extract the uncertain evidence of local semantic components, represented by the probability of visual object presence, using Single Shot MultiBox Detector (SSD). Finally, a novel uncertain belief propagation algorithm is introduced to propagate the belief of extracted uncertain evidence until convergence, then identifying the pornographic possibility of the image according to the final confidence. To evaluate EFUI, we employ the public NPDI Pornography database and collect a practical dataset contains 10 million images. Experimental results on all datasets demonstrate that our EFUI achieves the state-of-the-art performance on all evaluation metrics, and outperforms the best counterparts by up 6.95% and 0.61% for accuracy on the two datasets respectively. It also presents a comparable performance over the state-of-the-art approaches in literature. (C) 2018 Published by Elsevier B. V.