计算机科学 ›› 2019, Vol. 46 ›› Issue (12): 279-285.doi: 10.11896/jsjkx.190200315

• 图形图像与模式识别 • 上一篇    下一篇

使用模糊聚类的胶囊网络在图像分类上的研究

张天柱, 邹承明   

  1. (武汉理工大学计算机科学与技术学院 武汉430070)
  • 收稿日期:2019-02-18 出版日期:2019-12-15 发布日期:2019-12-17
  • 通讯作者: 邹承明(1975-),博士,教授,CCF会员,主要研究方向为计算机视觉、嵌入式系统等,E-mail:zoucm@whut.edu.cn。
  • 作者简介:张天柱(1993-),男,硕士生,主要研究方向为深度学习、计算机视觉。
  • 基金资助:
    本文受湖北省自然科学基金资助项目(2017CFB302)资助。

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

摘要: 胶囊网络中动态路由的本质就是聚类算法思想的实现。考虑到已有胶囊网络中的聚类方式需要数据满足一定的分布才能达到最好的效果,且图像特征比较复杂,于是将一种普适性更好的模糊聚类算法作为胶囊网络中的特征整合方式,并添加了一个使用信息熵来度量不确定性的激活值,以区分同一级别胶囊层特征的显著性。同时,借鉴特征金字塔网络的思想,将不同胶囊层的特征采样成相同尺度,然后进行融合独立训练。基于Keras框架进行实验,其结果表明,相比原来的胶囊网络,这种具有新型结构的胶囊网络在MNIST和CIFAR-10上有更高的识别准确率。对比实验证明了模糊聚类算法在胶囊网络上的应用潜力,其改善了原胶囊网络中聚类算法存在局限的问题;也证明了对胶囊网络中不同层的特征进行融合,能够获得包含信息更丰富且表达能力更强的特征。

关键词: 多尺度特征融合, 胶囊网络, 模糊聚类算法, 特征金字塔网络, 图像分类

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

中图分类号: 

  • 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.
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