Rock Thin Section Image Classification Research from Shallow Network to Deep Neural Network
Rongfang Gao, Chunxu Ji, Xinjian Qiang, Guojian Cheng, Ye Liu
Available Online May 2016.
- https://doi.org/10.2991/icemc-16.2016.125How to use a DOI?
- Rock thin section image classification; Shallow network; BP neural network; Deep neural network; Convolutional neural network; Deep learning
- Image classification technology has made tremendous progress with the development from shallow network (neural network) to deep neural network, image classification based on deep neural network has become popular in the field of image classification technology. By introducing shallow network and deep neural network, and making classification for 30 rock thin section images and contrast according to connectivity of pores with examples of the BP neural network (hidden layer has 6 neuron nodes and 7 neuron nodes) from shallow network and the convolutional neural network from deep neural network, finally, the average error rate of classification for deep neural network is 0%, and the average error rate of classification for BP neural network are 24.666% and 19.334% respectively, which shows that rock thin section image classification based on deep neural network has higher efficiency and better classification result than rock thin section image classification based on shallow network.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Rongfang Gao AU - Chunxu Ji AU - Xinjian Qiang AU - Guojian Cheng AU - Ye Liu PY - 2016/05 DA - 2016/05 TI - Rock Thin Section Image Classification Research from Shallow Network to Deep Neural Network BT - Proceedings of the 2016 International Conference on Education, Management and Computer Science PB - Atlantis Press SP - 620 EP - 625 SN - 1951-6851 UR - https://doi.org/10.2991/icemc-16.2016.125 DO - https://doi.org/10.2991/icemc-16.2016.125 ID - Gao2016/05 ER -