Adult image and video recognition by a deep multicontext network and fine-to-coarse strategy

Adult image and video recognition is an important and challenging problem in the real world. Low-level feature cues do not produce good enough information, especially when the dataset is very large and has various data distributions. This issue raises a serious problem for conventional approaches. In this article, we tackle this problem by proposing a deep multicontext network with fine-to-coarse strategy for adult image and video recognition. We employ a deep convolution networks to model fusion features of sensitive objects in images. Global contexts and local contexts are both taken into consideration and are jointly modeled in a unified multicontext deep learning framework. To make the model more discriminative for diverse target objects, we investigate a novel hierarchical method, and a task-specific fine-to-coarse strategy is designed to make the multicontext modeling more suitable for adult object recognition. Furthermore, some recently proposed deep models are investigated. Our approach is extensively evaluated on four different datasets. One dataset is used for ablation experiments, whereas others are used for generalization experiments. Results show significant and consistent improvements over the state-of-the-art methods.