Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 238-243.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Algorithm of Multi-layer Forward Artificial Neural Network for Image Classification

GU Zhe-bin, CAO Fei-long   

  1. College of Sciences,China Jiliang University,Hangzhou 310018,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: The input of traditional artificial neural network is in vector form,but the image is represented by matrix.Therefore,in the process of image processing,the image will be inputted into the neural network in vector form,which will destroy the structure information of image,and thus affect the effect of image processing.In order to improve the ability of network on image processing,the multilayer feedforward neural networks with matrix inputs are introducedbased on the idea and method of deep learning.At the same time,the traditional back-propagation algorithm (BP) is used to train the network,and the training process and training algorithm are given.After a lot of experiments,the network structure with good performance were determined,and the numerical experiments were carried out on the USPS handwritten digital data set.The experimental results show that the proposed multilayer network has better classification results than the single hidden layer feed forward neural network with matrix input (2D-BP).In addition,to deal with the problem of color image classification,this paper provided an effective and feasible method,the new 2D-BP network,to deal with it

Key words: Multilayer feedforward neural networks, Learning algorithm, Image classification, Gradient optimization

CLC Number: 

  • TN911.71
[1]高隽,谢昭.图像理解理论与方法 [M].北京:科学出版社,2009.
[2]RICHARDS L E.Principal Component Analysis [J].Journal of Marketing Research,1988,38(22):41-64.
[3]STONE J.Principal Component Analysis and Factor Analysis[M].MIT Press,2004:129-135.
[4]LU J,PLATANIOTIS K N,VENETSANOPOULOS A N.Face recognition using LDA-based algorithms [J].IEEE Transactions on Neural Networks,2003,14(1):195-200.
[5]ZHENG W S,LAI J H,YUEN P C.GA-fisher:A new LDA-based face recognition algorithm with selection of principal components[J].IEEE Transactions on Systems Man & Cybernetics Part B,2005,35(5):1065-1078.
[6]VAPNIK V N.The nature of statistical learning theory[M].New York:Springer,2000.
[7]SHAKHNAROVICH G,DARRELL T,INDYK P.Nearest- neighbor methods in learning and vision[J].Pattern Analysis and Applications,2008,11(2):221-222.
[8]HORNIK K,STINCHCOMBE M,WHITE H.Multilayer feedforward networks are universal approximators[J].Neural Networks,1989,2(5):359-366.
[9]DAI K,ZHAO J,CAO F.A novel algorithm of extended neural networks for image recognition [J].Engineering Applications of Artificial Intelligence,2015,42(1):57-66.
[10]YAN X,YAN X,ZHANG L,et al.Feature extraction based on fuzzy 2DLDA[J].Neurocomputing,2010,73(10-12):1556-1561.
[11]SANGUANSAT P,ASDORNWISED W,JITAPUNKUL S,et al.Two-Dimensional Linear Discriminant Analysis of Principle Component Vectors for Face Recognition[J].IEICE-Transactions on Information and Systems,2006,E89-D(7):2164-2170.
[12]LU J,ZHAO J,CAO F.Extended feed forward neural networks with random weights for face recognition[J].Neurocomputing,2014,136(1):96-102.
[13]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.
[14]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc.2012:1097-1105.
[15]ZEILER M D,FERGUS R.Visualizing and Understanding Convolutional Networks[C]∥European Conference on Computer Vision.2013:818-833.
[16]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J/OL].[2015-04-10].https://arxiv.org/pdf/1409.1556v6.pdf.
[17]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2015:1-9.
[18]HE K,ZHANG X,REN S,et al.Deep Residual Learning for Ima-ge Recognition[C]∥Computer Vision and Pattern Recognition.IEEE,2016:770-778.
[19]HULL J J.A database for handwritten text recognition research[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16(5):550-554.
[20]KRIZHEVSKY,ALEX.Learning Multiple Layers of Features from Tiny Images[M].Toronto:University of Toronto,2009.
[1] WU Hao-hao and WANG Fang-shi. Application of Multi-scale Dilated Convolution in Image Classification [J]. Computer Science, 2020, 47(6A): 166-171.
[2] ZHANG Hua-li, KANG Xiao-dong, RAN Hua, WANG Ya-ge, LI Bo and BAI Fang. Comparative Study of DBN and CNN for Pulmonary Nodule Image Recognition [J]. Computer Science, 2020, 47(6A): 254-259.
[3] WANG Jun-qian, ZHENG Wen-xian, XU Yong. Novel Image Classification Based on Test Sample Error Reconstruction Collaborative Representation [J]. Computer Science, 2020, 47(6): 104-113.
[4] KONG Fang, LI Qi-zhi, LI Shuai. Survey on Online Influence Maximization [J]. Computer Science, 2020, 47(5): 7-13.
[5] LIU Jun-qi,LI Zhi,ZHANG Xue-yang. Review of Maritime Target Detection in Visible Bands of Optical Remote Sensing Images [J]. Computer Science, 2020, 47(3): 116-123.
[6] WANG Li-hua,DU Ming-hui,LIANG Ya-ling. Classification Net Based on Angular Feature [J]. Computer Science, 2020, 47(2): 83-87.
[7] MAN Rui, YANG Ping, JI Cheng-yu, XU Bo-wen. Survey of Classification Methods of Breast Cancer Histopathological Images [J]. Computer Science, 2020, 47(11A): 145-150.
[8] JIANG Ze-tao, QIN Jia-qi, HU Shuo. Multi-spectral Scene Recognition Method Based on Multi-way Convolution Neural Network [J]. Computer Science, 2019, 46(9): 265-270.
[9] XU Shu-yan, HAN Li-xin, XU Guo-xia. Domain Adaptation Algorithm Based on Tensor Decomposition [J]. Computer Science, 2019, 46(12): 89-94.
[10] ZHANG Tian-zhu, ZOU Cheng-ming. Study on Image Classification of Capsule Network Using Fuzzy Clustering [J]. Computer Science, 2019, 46(12): 279-285.
[11] WANG Yan, WU Xiao-fu. Novel Normalization Algorithm for Training of Deep Neural Networks with Small Batch Sizes [J]. Computer Science, 2019, 46(11A): 273-276.
[12] WANG Li-ping, GAO Rui-zhen, ZHANG Jing-jun, WANG Er-cheng. Crack Detection of Concrete Pavement Based on Convolutional Neural Network [J]. Computer Science, 2019, 46(11A): 584-589.
[13] CUI Lu,ZHANG Peng,CHE Jin. Overview of Deep Neural Network Based Classification Algorithms for Remote Sensing Images [J]. Computer Science, 2018, 45(6A): 50-53.
[14] LI Si-yao, ZHOU Hai-fang, FANG Min-quan. Research of Image Classification Algorithm Based on GPU [J]. Computer Science, 2018, 45(6A): 143-145.
[15] LI Chang-li, ZHANG Lin, FAN Tang-huai. Hyperspectral Image Classification Based on Adaptive Active Learning and Joint Bilateral Filtering [J]. Computer Science, 2018, 45(12): 223-228.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[3] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[4] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[5] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[6] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[7] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[8] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[9] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[10] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .