WEATHER CLASSIFICATION WITH DEEP CONVOLUTIONAL NEURAL NETWORKS

被引：0

作者：

Elhoseiny, Mohamed ^{[1
]}

Huang, Sheng ^{[2
]}

Elgammal, Ahmed ^{[1
]}

机构：

[1] Rutgers State Univ, Piscataway, NJ 08854 USA

[2] Chongqing Univ, Chongqing 400044, Peoples R China

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2015年

关键词：

Deep Learning; Weather Classification; Image Classification; Convolutional Neural Networks; Image Convolutional Activation Feature;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：3349 / 3353

页数：5

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