TY - JOUR
T1 - Deep feature learnt by conventional deep neural network
AU - Niu, Huan
AU - Xu, Wei
AU - Akbarzadeh, Hamidreza
AU - Parvin, Hamid
AU - Beheshti, Amin
AU - Alinejad-Rokny, Hamid
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
KW - Intelligent filtering system
KW - Image classification
KW - Deep feature
KW - Deep neural network
KW - Convolutional neural network
KW - Data analytics
UR - http://www.scopus.com/inward/record.url?scp=85083395667&partnerID=8YFLogxK
U2 - 10.1016/j.compeleceng.2020.106656
DO - 10.1016/j.compeleceng.2020.106656
M3 - Article
VL - 84
JO - Computers and Electrical Engineering
JF - Computers and Electrical Engineering
SN - 0045-7906
M1 - 106656
ER -