计算机工程与应用2017，Vol.53Issue(13)：181-188,8.DOI:10.3778/j.issn.1002-8331.1601-0435
基于深度卷积神经网络的快速图像分类算法
Fast image classification algorithm based on deep convolutional neural network
摘要
Abstract
In order to solve large amount of images classification issues, a method is introduced by combining with CUDA-cuDNN and Deep Convolutional Neural Network(DCNN). This method makes advantages of DCNNs to learn features automatically, which makes up the incapability of hand-crafted features. Meanwhile, a cuDNN parallel computing method based on CUDA is employed to improve the speed of training and validation. DCNN is susceptible to parameter perturbation, which employs Batch Normalization(BN) to enhance the robustness. Experimental results indicate that the proposed method not only reduces training time substantially and accelerates validation speed, but also obtains lower classification error rate.关键词
深度卷积神经网络/CUDA-cuDNN方法/批量正则化/图像分类/深度学习Key words
Deep Convolutional Neural Network(DCNN)/CUDA-cuDNN/batch normalization/image classification/deep learning分类
信息技术与安全科学引用本文复制引用
王华利,邹俊忠,张见,卫作臣,汪春梅..基于深度卷积神经网络的快速图像分类算法[J].计算机工程与应用,2017,53(13):181-188,8.基金项目
国家自然科学基金(No.61071085) （No.61071085）
上海市教育委员会创新项目(No.14ZZ121). （No.14ZZ121）
