Open Access
ARTICLE
Advanced Feature Fusion Algorithm Based on Multiple Convolutional Neural Network for Scene Recognition
Lei Chen1, #, Kanghu Bo2, #, Feifei Lee1, *, Qiu Chen1, 3, *
1 School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and
Technology, Shanghai, 200093, China.
2 Algorithm Department, Unisoc, Shanghai, 201203, China.
3 Major of Electrical Engineering and Electronics, Graduate School of Engineering, Kogakuin University,
Sinjuku-ku, Tokyo, 163-8677, Japan.
# Both authors contributed equally to this work.
* Corresponding Authors: Feifei Lee. Email: ;
Qiu Chen. Email: .
Computer Modeling in Engineering & Sciences 2020, 122(2), 505-523. https://doi.org/10.32604/cmes.2020.08425
Received 24 August 2019; Accepted 23 October 2019; Issue published 01 February 2020
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.
Keywords
Cite This Article
Chen, L., Bo, K., Lee, F., Chen, Q. (2020). Advanced Feature Fusion Algorithm Based on Multiple Convolutional Neural Network for Scene Recognition.
CMES-Computer Modeling in Engineering & Sciences, 122(2), 505–523. https://doi.org/10.32604/cmes.2020.08425
Citations