文献类型：期刊文献

中文题名：基于深度卷积神经网络的快速图像分类算法

英文题名：Fast image classification algorithm based on deep convolutional neural network

作者：王华利[1];邹俊忠[1];张见[1];卫作臣[1];汪春梅[2]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]上海师范大学信息与机电工程学院,上海200234

年份：2017

卷号：53

期号：13

起止页码：181

中文期刊名：计算机工程与应用

外文期刊名：Computer Engineering and Applications

收录：CSTPCD;;北大核心:【北大核心2014】；CSCD:【CSCD_E2017_2018】；

基金：国家自然科学基金(No.61071085);上海市教育委员会创新项目(No.14ZZ121)

语种：中文

中文关键词：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

外文关键词：Deep Convolutional Neural Network(DCNN);;CUDA-cu DNN;;batch normalization;;image classification;;deep learning

摘要：为了应对大量图像的分类问题,提出一种基于深度卷积神经网络和CUDA-cuDNN并行运算的快速图像分类方法。该方法利用深度卷积神经网络自动学习特征的优势来解决手工设计特征普适性差等问题,同时结合基于CUDA架构的cuDNN并行运算策略来提高训练速度和加快分类速度,并且针对深度卷积神经网络易受参数扰动等缺点,引入批量正则化(Batch Normalization)以提高算法的鲁棒性。实验结果表明,该方法不仅大幅缩短了训练时间同时加快了图像的分类速度,而且进一步降低了图像分类的错误率。
In order to solve large amount of images classification issues, a method is introduced by combining with CUDAcu DNN 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 cu DNN 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.