Deep feature learnt by conventional deep neural network

• Published in: Computers & electrical engineering 2020-06, Vol.84, p.106656-11, Article 106656
• Main Authors: Niu, Huan, Xu, Wei, Akbarzadeh, Hamidreza, Parvin, Hamid, Beheshti, Amin, Alinejad-Rokny, Hamid
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Convolutional neural network; Data analytics; Datasets; Deep feature; Deep neural network; Image classification; Image filters; Intelligent filtering system; Medical imaging; Neural networks
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2020.106656

•As the pornographic sources have been proliferated on the Internet, it is needed to provide a system to detect and filter them accurately.•A new framework has been introduced to make use of the benefits of deep convolutional neural network and ensemble learning.•We show experimentally that the proposed model outperforms the state of the art.•The proposed approach may be used in different applications such as intelligent filtering of unconventional images or medical images analysis. In this paper, we introduce an approach to discriminate unconventional images and their intelligent filtering. As the target data to this issue are huge and consequently, a handling approach might potentially be a very time consuming one, one of the major challenges to be solved by this introduced approach is its ability for dealing with large-scale datasets. A deep neural network might be a good option to resolve this challenge. It can provide a good accuracy while dealing with huge databases. In the proposed approach, the new architecture is introduced using a combination of AlexNet and LeNet architectures. It uses convolutional, polling and fully-connected layers. The results are tested on two large-scale datasets. These tests show that the introduced architecture is more accurate than the other recently developed methods in identifying unconventional images. The proposed approach may be used in different applications such as intelligent filtering of unconventional images or medical images analysis. [Display omitted]