Adult content image recognition by Boltzmann machine limited and deep learning

Adult content images have a detrimental effect on Internet users, a significant number of whom are minors. Therefore, it is essential to control and detecting adult content images using multimedia processing and computer vision techniques. Previous studies have typically focused on manual-engineered visual features that may be difficult to detect and analyze. This paper presents a new model that employs deep convolutional neural networks within a Gaussian-Bernoulli limited-time, for adult content image recognition of a wide variety in a precise and effective manner. There are various layers within Convolutional Neural Networks for feature extraction and classification. Gaussian-Bernoulli limited-time was used for feature extraction to describe the images, and these features were summarized using the Boltzmann machine limited in the feature summary phase. The benefit of such an approach is convenience in carrying out feature extraction. Additionally, when tested on the most modern criterion dataset, this finding is believed to be more precise compared to other state-of-the-art approaches. The results obtained prove that the proposed approach leads to a higher efficiency.