Scene nudity level detection with deep nets
In this paper, we present an approach that can detect scene nudity level with high precision using different deep net configurations. For this purpose, a recent approach [1] which has intense and very deep convolution layers is used. During net modelling, we strive to obtain most successful net configuration by comparing different Dropout models and image sizes -64 × 64, 128 × 128-. Additionally, leveraging the generalization capability of Support Vector Machine (SVM), improvement on success rate is demonstrated by retraining the features obtained at different output levels of the nets with SVM. At test and training stages, scene is investigated under three nudity levels: regular, semi-nudity and full-nudity. In order to evaluate false alarm rates of the net models, tests are conducted on different datasets which are SUN2012, LEAR Human and a dataset contains only semi-nudity samples besides validation set determined for each class. The results indicate that high precision rates can be achieved with low false alarm rate exploiting deep net models.