Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (12): 279-285.doi: 10.11896/jsjkx.190200315

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Study on Image Classification of Capsule Network Using Fuzzy Clustering

ZHANG Tian-zhu, ZOU Cheng-ming   

  1. ( School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070,China)
  • Received:2019-02-18 Online:2019-12-15 Published:2019-12-17

PDF (PC)

1261

Abstract: The essence of dynamic routing in capsule network is the implementation of clustering algorithm.Considering that the clustering method used in the previous capsule network requires the data to meet certain distributions to achieve the best effect while features of image are complicated,a more universal fuzzy clustering algorithm was taken as the feature integration scheme to replace the old in this paper.And an activation value using information entropy to measure the indeterminacy was added to the model,so as to distinguish the significance of capsule features at the same layer.Meanwhile,drawing on the idea of feature pyramid network,the features of different capsule layers are sampled to the same size to fuse and then are trained independently.Experimental results based on the Keras framework show that the capsule network with new structure has higher recognition accuracy on MNIST and CIFAR-10 than the original capsule network.The contrast experiments prove great potential of fuzzy clustering algorithm applying on capsule network,which alleviates the limitation of the clustering algorithm in the original capsule network.The results also prove that the features of different layers in the capsule network can be fused to be more informative and expressive.

Key words: Capsule network, Feature pyramid network, Fuzzy clustering algorithm , Image classification, Multi-scale feature fusion

CLC Number: 

  • TP391
[1]LU D,WENG Q.A Survey of Image Classification Methods and Techniques for Improving Classification Performance[J].International Journal of Remote Sensing,2007,28(5):823-870.
[2]YANN L,BOTTOU L,BENGIO Y,et al.Gradient-based Learning Applied to Document Recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[3]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet Classification with Deep Convolutional Neural Networks[C]//NIPS.New York,NY:Curran Associates,2012:1097-1105.
[4]ZEILER M D,FERGUS R.Visualizing and Understanding Convolutional Networks[C]//Europen Conference on Computer Vision.Switzerland:Springer,2014:818-833.
[5]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-scale Image Recognition[C]//Computer Vision and Pattern Recognition.Princeton:CVPR Press,2015.
[6]SZEGEDY C,LIU W,JIA Y Q,et al.Going Deeper with Convolutions[C]//CVPR.Piscataway,NJ:IEEE,2015:1-9.
[7]HE K,ZHANG X Y,REN S Q,et al.Deep Residual Learning for Image Recognition[C]//CVPR.Piscataway,NJ:IEEE,2016:770-778.
[8]HUANG G,LIU Z,MAATEN L,et al.Densely Connected Convolutional Networks[C]//Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2017:2261-2269.
[9]GU Z B,CAO F L.Image Classification algorithm of Multi-layer Forward Artificial Neural Network [J].Computer Science,2018,45(S2):238-243.(in Chinese)
顾哲彬,曹飞龙.多层前向人工神经网络图像分类算法[J].计算机科学,2018,45(S2):238-243.
[10]HINTON G E,SRIVASTAVA N,KRIZHEVSKY A,et al.Improving Neural Networks by Pre-venting Co-adaptation of Feature Detectors[C/OL].http://arxiv.org/abs/1207.0580.
[11]IOFFE S.SZEGEDY C.Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift[C]//International Conference on International Conference on Machine Learning.New York,NY:ACM,2015:448-456.
[12]TONG W G,LI M X,ZHANG Y K.Research on Optimization Algorithm of Deep Learning[J].Computer Science,2018,45(S2):155-159.(in Chinese)
仝卫国,李敏霞,张一可.深度学习优化算法研究[J].计算机科学,2018,45(S2):155-159.
[13]FU B W,SUN T,LIANG J,et al.Review of Principle and Application of Deep Learning[J].Computer Science,2018,45(S1):11-15.(in Chinese)
付文博,孙涛,梁藉,等.深度学习原理及应用综述[J].计算机科学,2018,45(S1):11-15.
[14]SABOUR S,FROSST N,HINTON G E.Dynamic Routing between Capsules[C]//NIPS.New York,NY:Curran Associates,2017:3859-3869.
[15]HINTON G E,SABOUR S,FROSST N.Matrix Capsules with EM Routing[C]//International Conference on Learning Representations.Princeton:ICLR Press,2018.
[16]JAISWAL A,ABDALMAGEED W,WU Y,et al.CapsuleGAN:Generative Adversarial Capsule Network[C]//EuropenConfe-rence on Computer Vision.Berlin:Springer,2018:526-535.
[17]DENG F,PU S L,CHEN X H,et al.Hyperspectral Image Classification with Capsule Network Using Limited Training Samples[J].Sensors,2018,18(9):3153.
[18]ZHANG X N,LUO P C,HU X W,et al.Research on Classification Performance of Small-Scale Dataset Based on Capsule Network[C]//International Conference on Robotics and Artificial Intelligence.New York,NY:ACM,2018:24-28.
[19]XIANG C Q,ZHANG L,TANG Y,et al.MS-CapsNet:A Novel Multi-Scale Capsule Network[J].IEEE Signal Processing Letters,2018,25(12):1850-1854.
[20]BEZDEK J C,EHRLICH R,FULL W.FCM:The Fuzzy c-means Clustering Algorithm[J].Computers & Geosciences,1984,10(2/3):191-203.
[21]ADELSON E H,ANDERSON C H,BERGEN J R,et al.Pyramid Methods in Image Processing[J].RCA engineer,1984,29(6),33-41.
[22]BELL S,ZITNICK C L,BALA K,et al.Inside-Outside Net:Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks[C]//International Conference on Learning Representations.Piscataway,NJ:IEEE,2016:2874-2883.
[23]KONG T,YAO A B,CHEN Y R,et al.HyperNet:Towards Accurate Region Proposal Generation and Joint Object Detection[C]//International Conference on Learning Representations.Piscataway,NJ:IEEE,2016:845-853.
[24]LIN T Y,DOLLAR P,GRISHICK R,et al.Feature Pyramid Networks for Object Detection[C]//International Conference on Learning Representations.Piscataway,NJ:IEEE,2017:936-944.
[25]FRANCIS D,HUET B,MERIALDO B.Embedding Images and Sentences in a Common Space with a Recurrent Capsule Network[C]//International Workshop on Content-based Multimedia Indexing.Piscataway,NJ:IEEE,2018:1-6.
[1] WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25.
[2] DU Li-jun, TANG Xi-lu, ZHOU Jiao, CHEN Yu-lan, CHENG Jian. Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning [J]. Computer Science, 2022, 49(6A): 60-65.
[3] YANG Jian-nan, ZHANG Fan. Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure [J]. Computer Science, 2022, 49(6A): 353-357.
[4] ZHU Xu-dong, XIONG Yun. Study on Multi-label Image Classification Based on Sample Distribution Loss [J]. Computer Science, 2022, 49(6): 210-216.
[5] WU Lin, SUN Jing-yu. Multi-branch RA Capsule Network and Its Application in Image Classification [J]. Computer Science, 2022, 49(6): 224-230.
[6] PENG Yun-cong, QIN Xiao-lin, ZHANG Li-ge, GU Yong-xiang. Survey on Few-shot Learning Algorithms for Image Classification [J]. Computer Science, 2022, 49(5): 1-9.
[7] FAN Xin-nan, ZHAO Zhong-xin, YAN Wei, YAN Xi-jun, SHI Peng-fei. Multi-scale Feature Fusion Image Dehazing Algorithm Combined with Attention Mechanism [J]. Computer Science, 2022, 49(5): 50-57.
[8] ZHANG Wen-xuan, WU Qin. Fine-grained Image Classification Based on Multi-branch Attention-augmentation [J]. Computer Science, 2022, 49(5): 105-112.
[9] XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo. Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [J]. Computer Science, 2022, 49(3): 288-293.
[10] SHAO Hai-lin, JI Yi, LIU Chun-ping, XU Yun-long. Scene Text Detection Algorithm Based on Enhanced Feature Pyramid Network [J]. Computer Science, 2022, 49(2): 248-255.
[11] DONG Lin, HUANG Li-qing, YE Feng, HUANG Tian-qiang, WENG Bin, XU Chao. Survey on Generalization Methods of Face Forgery Detection [J]. Computer Science, 2022, 49(2): 12-30.
[12] FAN Hong-jie, LI Xue-dong, YE Song-tao. Aided Disease Diagnosis Method for EMR Semantic Analysis [J]. Computer Science, 2022, 49(1): 153-158.
[13] CHEN Tian-rong, LING Jie. Differential Privacy Protection Machine Learning Method Based on Features Mapping [J]. Computer Science, 2021, 48(7): 33-39.
[14] CHEN Zhi-wen, WANG Kun, ZHOU Guang-yun, WANG Xu, ZHANG Xiao-dan, ZHU Hu-ming. SAR Image Change Detection Method Based on Capsule Network with Weight Pruning [J]. Computer Science, 2021, 48(7): 190-198.
[15] HU Jing-hui, XU Peng. Automatic Classification of Aviation Fastener Products Based on Image Classification [J]. Computer Science, 2021, 48(6A): 63-66.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: [email protected]
Powered by Beijing Magtech Co., Ltd.