摘要
针对卷积神经网络主要使用图像的局部特征而忽略图像通道特征的不足,提出一种分离多路卷积神经网络。提取通道特征与卷积特征,并在全连接层进行融合,以此提升该网络的图像识别与分类效果。在cifar10和SVHN数据集上进行的实验结果表明,与ResNet,Network in Network,Maxout等8种卷积神经网络相比,该网络的平均识别率较高。
As the Convolutional Neural Network （CNN） mainly uses the local features of the image, ignoring image channel features, this paper proposes a Detached Multiple Convolutional Neural Network （DMCNN）. It extracts the channel features and convolution features, and fuses them in the whole connection layer so that image recognition and classification effects of the proposed network are improved. The experimental results on cifarl0 and SVHN datasets show that the average recognition rate of the network is higher than that of other 8 CNNs like RexNet,Network in Network, Maxout.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第6期145-149,157,共6页
Computer Engineering
基金
国家自然科学基金(61262006
61540050)
贵州省重大应用基础研究项目(黔科合JZ字[2014]2001)
贵州省科技厅联合基金(黔科合LH字[2014]7636号)
关键词
卷积神经网络
深度学习
特征提取
图像分类
图像识别
通道特征
Convolutional Neural Network （ CNN ）
deep learning
feature extraction
image classification
imagerecognition
channel characteristic
