端到端双通道特征重标定DenseNet图像分类  

Image classification method based on end-to-end dual feature reweight DenseNet

作　　者：郭玉荣 张珂[1] 王新胜[1] 苑津莎[1] 赵振兵[1] 马占宇 Guo Yurong;Zhang Ke;Wang Xinsheng;Yuan Jinsha;Zhao Zhenbing;Ma Zhanyu(The Department of Electronic and Communication Engineering,North China Electric Power University,Baoding 071000,China;School of Information and Communication Engineering,Institute of Artificial Intelligence,Beijing University of Posts and Telecommunication,Beijing 100086,China)

机构地区：[1]华北电力大学电子与通信工程系,保定071000 [2]北京邮电大学信息与通信工程学院人工智能研究院,北京100086

出　　处：《中国图象图形学报》2020年第3期486-497,共12页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61871182,61922015,61773071,61302163);河北省自然科学基金项目(F2015502062,F2016502101,F2017502016);北京市自然科学基金项目(4192055);中央高校基本科研经费项目(2018MS094,2018MS095)。

摘　　要：目的针对密集连接卷积神经网络(Dense Net)没有充分考虑通道特征相关性以及层间特征相关性的缺点,本文结合软注意力机制提出了端到端双通道特征重标定密集连接卷积神经网络。方法提出的网络同时实现了Dense Net网络的通道特征重标定与层间特征重标定。给出了Dense Net网络通道特征重标定与层间特征重标定方法;构建了端到端双通道特征重标定密集连接卷积神经网络,该网络每个卷积层的输出特征图经过两个通道分别完成通道特征重标定以及层间特征重标定,再进行两种重标定后特征图的融合。结果为了验证本文方法在不同图像分类数据集上的有效性和适应性,在图像分类数据集CIFAR-10/100以及人脸年龄数据集MORPH、Adience上进行了实验,提高了图像分类准确率,并分析了模型的参数量、训练及测试时长,验证了本文方法的实用性。与Dense Net网络相比,40层及64层双通道特征重标定密集连接卷积神经网络DFR-DenseNet (dual feature reweight Dense Net),在CIFAR-10数据集上,参数量仅分别增加1.87%、1.23%,错误率分别降低了12%、9.11%,在CIFAR-100数据集上,错误率分别降低了5.56%、5.41%;与121层DFR-DenseNet网络相比,在MORPH数据集上,平均绝对误差(MAE)值降低了7.33%,在Adience数据集上,年龄组估计准确率提高了2%;与多级特征重标定密集连接卷积神经网络MFR-DenseNet(multiple feature reweight Dense Net)相比,DFR-DenseNet网络参数量减少了一半,测试耗时约缩短为MFR-DenseNet的61%。结论实验结果表明本文端到端双通道特征重标定密集连接卷积神经网络能够增强网络的学习能力,提高图像分类的准确率,并对不同图像分类数据集具有一定的适应性、实用性。Objective Image classification is one of the important research technologies in computer vision. The development of deep learning and convolutional neural networks(CNNs) has laid the technical foundation for image classification. In recent years,image classification methods based on deep CNN have become an important research topic. DenseN et is one of the widely applied deep CNNs in image classification,encouraging feature reusage and alleviating the vanishing gradient problem. However,this approach has obvious limitations. First,each layer simply combines the feature maps obtained from preceding layers by concatenating operation without considering the interdependencies between different channels. The network representation can be further improved by modeling feature channel correlation and realizing channel feature recalibration. Second,the correlation of the interlayer feature map is not explicitly modeled. Thus,adaptively learning the correlation coefficients by modeling the correlation of feature maps between the layers is important. Method The conventional DenseN et networks do not adequately consider the channel feature correlation and interlayer feature correlation. To address these limitations,multiple feature reweight DenseN et(MFR-DenseN et) combines channel feature reweight DenseN et(CFR-DenseN et) and inter-layer feature reweight DenseN et(ILFR-DenseN et) by ensemble learning method,thereby improving the representation power of the DenseN et by adaptively recalibrating the channel-wise feature responses and explicitly modeling the interdependencies between the features of different convolutional layers. However,MFR-DenseN et uses two independent parallel networks for image classification,which is not end-to-end training. The CFR-DenseN et and the ILFR-DenseN et models should be trained and saved in training. First,the models and weights are loaded,and the MFR-DenseN et needs multiple save and load. The training process is cumbersome. Second,the parameters and calculations are large,so the training ta

关 键 词：双通道特征重标定密集连接卷积神经网络 通道特征重标定 层间特征重标定 图像分类 端到端 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]