[1]高淑萍,赵清源,齐小刚,等.改进MobileNet的图像分类方法研究[J].智能系统学报,2021,16(1):11-20.[doi:10.11992/tis.202012034]
 GAO Shuping,ZHAO Qingyuan,QI Xiaogang,et al.Research on the improved image classification method of MobileNet[J].CAAI Transactions on Intelligent Systems,2021,16(1):11-20.[doi:10.11992/tis.202012034]
点击复制

改进MobileNet的图像分类方法研究

参考文献/References:
[1] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[2] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with con-volutions[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015:1-9.
[3] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:770-778.
[4] HAN K, WANG Y, TIAN Q, et al. GhostNet:More features from cheap operations[J]. arXiv preprint arXiv:1911.11907, 2019.
[5] ZHANG X, ZHOU X, LIN M, et al. Shufflenet:an extremely efficient convolutional neural network for mobile devic-es[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:6848-6856.
[6] HOWARD A G, ZHU Menglong, CHEN Bo, et al. MobileNets:efficient convolutional neural networks for mobile vision applications[J]. arXiv:1704.04861, 2017.
[7] SANDLER M, HOWARD A, ZHU Menglong, et al. MobileNetv2:inverted residuals and linear bottlenecks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:4510-4520.
[8] MA Ningning, ZHANG Xiangyu, ZHENG Haitao, et al. ShuffleNet V2:practical guidelines for efficient CNN architecture design[C]//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany, 2018:116-131.
[9] IANDOLA F N, HAN Song, MOSKEWICZ M W, et al. SqueezeNet:AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size[J]. arXiv:1602.07360, 2016.
[10] SUN Ke, LI Mingjie, LIU Dong, et al. IGCV3:interleaved low-rank group convolutions for efficient deep neural networks[J]. arXiv:1806.00178, 2018.
[11] 黄跃珍, 王乃洲, 梁添才, 等. 基于改进型MobileNet网络的车型识别方法[J]. 电子技术与软件工程, 2019(1):22-24
HUANG Yuezhen, WANG Naizhou, LIANG Tiancai, et al. Vehicle identification method based on improved mobilenet network[J]. Electronic technology and software engineering, 2019(1):22-24
[12] 刘鸿智. 面向移动设备的轻型神经网络的改进与实现[D]. 呼和浩特:内蒙古大学, 2019.
LIU Hongzhi. Improvement and implementation of lightweight neural network for mobile devices[D]. Hohhot:Inner Mongolia University, 2019.
[13] 郭奕君, 努尔毕亚·亚地卡尔, 朱亚俐, 等. 基于MobileNet网络多国人脸分类识别[J]. 图像与信号处理, 2020, 9(3):146-155.GUO Yijun, ABUDIRIYIMU A, YADIKAR N, et al. Multinational face classification and recognition based on MobileNet network[J]. Journal of image and signal processing, 2020, 9(3):146-155.
[14] DUBEY A K, JAIN V. Comparative study of convolution neural network’s relu and leaky-relu activation functions[M]. MISHRA S, SOOD Y R, TOMAR A. Applications of Computing, Automation and Wireless Systems in Electrical Engineering. Singapore:Springer, 2019:873-880.
[15] CHEN Yinpeng, DAI Xiyang, LIU Mengchen, et al. Dynamic ReLU[J]. arXiv:2003.10027, 2020.
[16] KRIZHEVSKY A. Learning multiple layers of features from tiny images[R]. Toronto:University of Toronto, 2009.
[17] DUGAN P, CUKIERSKI W, SHIU Y, et al. Kaggle competition[J]. Cornell University, the ICML, 2013.
[18] XU Bing, WANG Naiyan, CHEN Tianqi, et al. Empirical evaluation of rectified activations in convolutional network[J]. arXiv:1505.00853, 2015.
[19] RANZATO M A, HINTON G E. Modeling pixel means and covariances using factorized third-order Boltzmann machines[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010:2551-2558.
[20] YU Kai, ZHANG Tong. Improved local coordinate Coding using local tangents[C]//Proceedings of the 27th International Conference on Machine Learning. Haifa, Israel, 2010.
[21] CHAN T H, JIA Kui, GAO Shenghua, et al. PCANet:a simple deep learning baseline for image classification?[J]. IEEE transactions on image processing, 2015, 24(12):5017-5032.
[22] ZEILER M D, FERGUS R. Stochastic pooling for regularization of deep convolutional neural networks[J]. arXiv:1301.3557, 2013.
[23] 刘金利, 张培玲. 改进LeNet-5网络在图像分类中的应用[J]. 计算机工程与应用, 2019, 55(15):32-37, 95.
LIU Jinli, ZHANG Peiling. Application of improved LeNet-5 network in image classification[J]. Computer engineering and applications, 2019 (15):5.
[24] GOODFELLOW I J, WARDE FARLEY D, MIRZA M, et al. Maxout networks[J]. arXiv preprint arXiv:1302.4389, 2013.
[25] LIN Min, CHEN Qiang, YAN Shuicheng. Network in network[J]. arXiv:1312.4400, 2013.
[26] SRIVASTAVA R K, GREFF K, SCHMIDHUBER J. Highway networks[J]. arXiv:1505.00387, 2015.
相似文献/References:
[1]李海峰,杜军平.颜色特征的图像分类技术研究[J].智能系统学报,2008,3(2):65.[doi:CNKI:SUN:ZNXT.0.2008-02-017]
[2]李海峰,杜军平.颜色特征的图像分类技术研究[J].智能系统学报,2008,3(2):155.
 LI Hai-feng,DU Jun-ping.Image classification technology based on color features[J].CAAI Transactions on Intelligent Systems,2008,3(1):155.
[3]姚伏天,钱沄涛.高斯过程及其在高光谱图像分类中的应用[J].智能系统学报,2011,6(5):396.
 YAO Futian,QIAN Yuntao.Gaussian process and its applications in hyperspectral image classification[J].CAAI Transactions on Intelligent Systems,2011,6(1):396.
[4]尤雅萍,成运,苏松志,等.基于谱域-空域结合特征和图割原理的高光谱图像分类[J].智能系统学报,2015,10(2):201.[doi:10.3969/j.issn.1673-4785.201410040]
 YOU Yaping,CHENG Yun,SU Songzhi,et al.Hyperspectral image classification based on spectral-spatial combination features and graph cut[J].CAAI Transactions on Intelligent Systems,2015,10(1):201.[doi:10.3969/j.issn.1673-4785.201410040]
[5]殷瑞,苏松志,李绍滋.一种卷积神经网络的图像矩正则化策略[J].智能系统学报,2016,11(1):43.[doi:10.11992/tis.201509018]
 YIN Rui,SU Songzhi,LI Shaozi.Convolutional neural network’s image moment regularizing strategy[J].CAAI Transactions on Intelligent Systems,2016,11(1):43.[doi:10.11992/tis.201509018]
[6]龚震霆,陈光喜,任夏荔,等.基于卷积神经网络和哈希编码的图像检索方法[J].智能系统学报,2016,11(3):391.[doi:10.11992/tis.201603028]
 GONG Zhenting,CHEN Guangxi,REN Xiali,et al.An image retrieval method based on a convolutional neural network and hash coding[J].CAAI Transactions on Intelligent Systems,2016,11(1):391.[doi:10.11992/tis.201603028]
[7]刘帅师,程曦,郭文燕,等.深度学习方法研究新进展[J].智能系统学报,2016,11(5):567.[doi:10.11992/tis.201511028]
 LIU Shuaishi,CHENG Xi,GUO Wenyan,et al.Progress report on new research in deep learning[J].CAAI Transactions on Intelligent Systems,2016,11(1):567.[doi:10.11992/tis.201511028]
[8]师亚亭,李卫军,宁欣,等.基于嘴巴状态约束的人脸特征点定位算法[J].智能系统学报,2016,11(5):578.[doi:10.11992/tis.201602006]
 SHI Yating,LI Weijun,NING Xin,et al.A facial feature point locating algorithmbased on mouth-state constraints[J].CAAI Transactions on Intelligent Systems,2016,11(1):578.[doi:10.11992/tis.201602006]
[9]赵骞,李敏,赵晓杰,等.基于感受野学习的特征词袋模型简化算法[J].智能系统学报,2016,11(5):663.[doi:10.11992/tis.201601001]
 ZHAO Qian,LI Min,ZHAO Xiaojie,et al.Learning receptive fields for compact bag-of-feature model[J].CAAI Transactions on Intelligent Systems,2016,11(1):663.[doi:10.11992/tis.201601001]
[10]费宇杰,吴小俊.一种局部聚合描述符和组显著编码相结合的编码方法[J].智能系统学报,2017,12(2):172.[doi:10.11992/tis.201602010]
 FEI Yujie,WU Xiaojun.A new feature coding algorithm based on the combination of group salient coding and VLAD[J].CAAI Transactions on Intelligent Systems,2017,12(1):172.[doi:10.11992/tis.201602010]
[11]赵玉新,赵廷.海底声呐图像智能底质分类技术研究综述[J].智能系统学报,2020,15(3):587.[doi:10.11992/tis.202004026]
 ZHAO Yuxin,ZHAO Ting.Survey of the intelligent seabed sediment classification technology based on sonar images[J].CAAI Transactions on Intelligent Systems,2020,15(1):587.[doi:10.11992/tis.202004026]
[12]郭磊,王骏,丁维昌,等.4D卷积神经网络的自闭症功能磁共振图像分类[J].智能系统学报,2021,16(6):1021.[doi:10.11992/tis.202009022]
 GUO Lei,WANG Jun,DING Weichang,et al.Classification of the functional magnetic resonance image of autism based on 4D convolutional neural network[J].CAAI Transactions on Intelligent Systems,2021,16(1):1021.[doi:10.11992/tis.202009022]

备注/Memo

收稿日期:2020-12-31。
基金项目:国家自然科学基金项目(91338115);高等学校学科创新引智基地“111”计划(B08038)
作者简介:高淑萍,教授,主要研究方向为多目标优化理论与应用、数学与信息科学交叉研究、大数据处理与分析。主持、参与国家级和省自然科学基金项目及横向项目多项。发表学术论文30余篇;赵清源,硕士研究生,主要研究方向为深度学习、图像分类、算法优化;齐小刚,教授,博士生导师,主要研究方向为复杂系统建模与仿真、网络算法设计与应用。申请专利47项(授权19项),登记软件著作权4项。发表学术论文100余篇
通讯作者:赵清源. E-mail:zqy353364144@163.com

更新日期/Last Update: 2021-02-25
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134