Modified Algorithm of Capsule Network for Classifying Small Sample Image

doi:10.3778/j.issn.1673-9418.2102026

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (10): 2387-2394.DOI: 10.3778/j.issn.1673-9418.2102026

• Graphics and Image • Previous Articles Next Articles

Modified Algorithm of Capsule Network for Classifying Small Sample Image

WANG Feilong¹, LIU Ping¹, ZHANG Ling², LI Gang²^,⁺()

1. College of Data Science, Taiyuan University of Technology, Jinzhong, Shanxi 030600, China
2. College of Software, Taiyuan University of Technology, Jinzhong, Shanxi 030600, China

Received:2021-02-06 Revised:2021-04-12 Online:2022-10-01 Published:2021-04-26
About author:WANG Feilong, born in 1996, M.S. candidate, student member of CCF. His research interest is image processing.
LIU Ping, born in 1976, Ph.D. candidate, asso-ciate professor. Her research interest is big data of water resources.
ZHANG Ling, born in 1985, Ph.D. candidate, lecturer, member of CCF. Her research interests include machine learning and image processing.
LI Gang, born in 1980, Ph.D. candidate, asso-ciate professor, member of CCF. His research interests include artificial intelligence and vi-sual information processing.
Supported by:
National Natural Science Foundation of China(61976150);Natural Science Foundation of Shanxi Province(201901D111091);Natural Science Foundation of Shanxi Province(201801D21135);University Science and Technology Innovation Project of Shanxi Province(JYTKJCX201943)

改进胶囊网络的小样本图像分类算法

王飞龙¹, 刘萍¹, 张玲², 李钢²^,⁺()

1.太原理工大学大数据学院,山西晋中 030600
2.太原理工大学软件学院,山西晋中 030600

通讯作者: + E-mail: ligang@tyut.edu.cn
作者简介:王飞龙（1996—）,男,山西运城人,硕士研究生,CCF学生会员,主要研究方向为图像处理。
刘萍（1976—）,女,山西忻州人,博士研究生,副教授,主要研究方向为水资源大数据。
张玲（1985—）,女,山西吕梁人,博士研究生,讲师,CCF会员,主要研究方向为机器学习、图像处理。
李钢（1980—）,男,内蒙古包头人,博士研究生,副教授,CCF 会员,主要研究方向为人工智能、视觉信息处理。
基金资助:
国家自然科学基金(61976150);山西省自然科学基金(201901D111091);山西省自然科学基金(201801D21135);山西省高校科技创新项目(JYTKJCX201943)

Abstract

Abstract:

In order to address the problem that the capsule network can not classify complex small sample images effectively, a classification model is proposed on the basis of fusing the improved Darknet with the capsule network. Firstly, the Darknet is upgraded containing both the shallow level extractor and the deep level extractor. The shallow level extractor adopts a 5×5 convolution kernel to capture long-distance edge contour features and the deep level extractor uses a 3×3 convolution kernel to capture deeper semantic features. Then, the extracted edge features and semantic features are fused to preserve effective features of images. Next, the capsule network is used to vectorize these effective features to work out the loss of spatial representation. Finally, L₂ regularization is added in the loss function to avoid the over-fitting. Experimental results show that, on the small sample dataset, the classification accuracy of the proposed model is 28.51 percentage points and 24.40 percentage points higher than that of the models of the capsule network and the DCaps respectively, 21.57 percentage points and 18.02 percentage points higher than that of the ResNet50 and the Xception respectively. Hence it suggests that the method proposed in this paper gains a better performance in classifying complex small sample images. Meanwhile, on the large sample dataset, the classification accuracy of the proposed model has also been improved to a certain extent.

Key words: small sample image, capsule network, Darknet, L₂ regularization, image classification

摘要：

为了解决胶囊网络不能对复杂的小样本图像进行有效分类的问题,提出一种将Darknet进行改进融入胶囊网络的分类模型。首先将Darknet改进为同时包含浅层与深层特征提取器的模型,浅层特征提取器采用5×5的卷积核以捕捉长距离的边缘轮廓特征,深层特征提取器采用3×3的卷积核以捕捉更深层的语义特征,再将图像的浅层边缘特征与深层语义特征进行融合,以保留图像的有效特征;接着利用胶囊网络对图像有效特征进行向量化处理,解决特征空间表征能力缺失的问题;最后在损失函数中加入L₂正则化项,避免模型的过拟合问题。实验结果表明,在小样本数据集上,该模型相比胶囊网络、DCaps模型分类准确率分别提升28.51个百分点和24.40个百分点,相比ResNet50、Xception等卷积神经网络分别提升21.57个百分点和18.02个百分点,显示该方法对复杂小样本图像分类性能提升明显;同时在大样本数据集上,该模型的分类性能也获得了一定程度的提升。

关键词: 小样本图像, 胶囊网络, Darknet, L₂正则化项, 图像分类

CLC Number:

TP391

WANG Feilong, LIU Ping, ZHANG Ling, LI Gang. Modified Algorithm of Capsule Network for Classifying Small Sample Image[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(10): 2387-2394.

王飞龙, 刘萍, 张玲, 李钢. 改进胶囊网络的小样本图像分类算法[J]. 计算机科学与探索, 2022, 16(10): 2387-2394.

Figures/Tables 11

References 15

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网络参数	CIFAR-10	CIFAR-100
seperable_conv2d+relu+BN+L₂	62.78	39.60
conv2d+selu+BN+L₂	76.29	43.24
conv2d+relu+GN+L₂	80.68	35.45
conv2d+relu+BN+L₁	76.93	30.07
conv2d+relu+BN+L₂₁	76.75	30.56
conv2d+relu+batch_norm+L₂	82.42	47.83

网络参数	CIFAR-10	CIFAR-100
seperable_conv2d+relu+BN+L₂	62.78	39.60
conv2d+selu+BN+L₂	76.29	43.24
conv2d+relu+GN+L₂	80.68	35.45
conv2d+relu+BN+L₁	76.93	30.07
conv2d+relu+BN+L₂₁	76.75	30.56
conv2d+relu+batch_norm+L₂	82.42	47.83

网络参数	CIFAR-10	CIFAR-100
seperable_conv2d+relu+BN+L₂	71.92	46.39
conv2d+selu+BN+L₂	79.39	49.86
conv2d+relu+GN+L₂	82.73	43.52
conv2d+relu+BN+L₁	80.21	42.24
conv2d+relu+BN+L₂₁	79.85	40.63
conv2d+relu+batch_norm+L₂	85.16	54.69

网络参数	CIFAR-10	CIFAR-100
seperable_conv2d+relu+BN+L₂	71.92	46.39
conv2d+selu+BN+L₂	79.39	49.86
conv2d+relu+GN+L₂	82.73	43.52
conv2d+relu+BN+L₁	80.21	42.24
conv2d+relu+BN+L₂₁	79.85	40.63
conv2d+relu+batch_norm+L₂	85.16	54.69

网络模型	CIFAR-10	CIFAR-100
CapsuleNet	55.13	26.18
DCaps	62.65	30.29
ResNet50	82.87	33.12
Xception	83.25	36.67
Cap-Dark-NR	80.66	47.78
Cap-Dark	82.42	47.83
Bi-Cap-Dark	85.16	54.69

Modified Algorithm of Capsule Network for Classifying Small Sample Image

改进胶囊网络的小样本图像分类算法

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