TY - GEN
T1 - Weather classification with deep convolutional neural networks
AU - Elhoseiny, Mohamed
AU - Huang, Sheng
AU - Elgammal, Ahmed
N1 - Generated from Scopus record by KAUST IRTS on 2019-11-20
PY - 2015/12/9
Y1 - 2015/12/9
N2 - In this paper, we study weather classification from images using Convolutional Neural Networks (CNNs). Our approach outperforms the state of the art by a huge margin in the weather classification task. Our approach achieves 82.2% normalized classification accuracy instead of 53.1% for the state of the art (i.e., 54.8% relative improvement). We also studied the behavior of all the layers of the Convolutional Neural Networks, we adopted, and interesting findings are discussed.
AB - In this paper, we study weather classification from images using Convolutional Neural Networks (CNNs). Our approach outperforms the state of the art by a huge margin in the weather classification task. Our approach achieves 82.2% normalized classification accuracy instead of 53.1% for the state of the art (i.e., 54.8% relative improvement). We also studied the behavior of all the layers of the Convolutional Neural Networks, we adopted, and interesting findings are discussed.
UR - http://ieeexplore.ieee.org/document/7351424/
UR - http://www.scopus.com/inward/record.url?scp=84956679640&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2015.7351424
DO - 10.1109/ICIP.2015.7351424
M3 - Conference contribution
SN - 9781479983391
BT - Proceedings - International Conference on Image Processing, ICIP
PB - IEEE Computer [email protected]
ER -