@Article{cmes.2020.08425,
AUTHOR = {Lei Chen, Kanghu Bo, Feifei Lee, Qiu Chen},
TITLE = {Advanced Feature Fusion Algorithm Based on Multiple Convolutional Neural Network for Scene Recognition},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {122},
YEAR = {2020},
NUMBER = {2},
PAGES = {505--523},
URL = {http://www.techscience.com/CMES/v122n2/38311},
ISSN = {1526-1506},
ABSTRACT = {Scene recognition is a popular open problem in the computer vision field. Among 
lots of methods proposed in recent years, Convolutional Neural Network (CNN) based 
approaches achieve the best performance in scene recognition. We propose in this paper an 
advanced feature fusion algorithm using Multiple Convolutional Neural Network (MultiCNN) for scene recognition. Unlike existing works that usually use individual convolutional 
neural network, a fusion of multiple different convolutional neural networks is applied for 
scene recognition. Firstly, we split training images in two directions and apply to three deep 
CNN model, and then extract features from the last full-connected (FC) layer and 
probabilistic layer on each model. Finally, feature vectors are fused with different fusion 
strategies in groups forwarded into SoftMax classifier. Our proposed algorithm is evaluated
on three scene datasets for scene recognition. The experimental results demonstrate the 
effectiveness of proposed algorithm compared with other state-of-art approaches.},
DOI = {10.32604/cmes.2020.08425}
}