[1]LIU Wei,WANG Xinyu,LIU Guangwei,et al.Semi-supervised image classification method fused with relational features[J].CAAI Transactions on Intelligent Systems,2022,17(5):886-899.[doi:10.11992/tis.202109022]
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Semi-supervised image classification method fused with relational features

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 5 Page number: 886-899 Column: 学术论文—机器学习 Public date: 2022-09-05
Title:
Semi-supervised image classification method fused with relational features
Author(s):
LIU Wei1; 2; 3;  WANG Xinyu1; 3;  LIU Guangwei4;  WANG Dong4;  NIU Yingjie1; 3
1. School of Sciences, Liaoning Technical University, Fuxin 123000, China;
2. Institutes of Intelligent Engineering and Mathematics, Liaoning Technical University, Fuxin 123000, China;
3. Institute of Mathematics and Systems Science, Liaoning Technical University, Fuxin 123000, China;
4. School of Mining, Liaoning Technical University, Fuxin 123000, China
Keywords:
relationship representation; feature extraction; graph convolutional neural network; hybrid model; semi-supervised learning; image classification; convolution in vision; generalization performance
CLC:
TP181
DOI:
10.11992/tis.202109022
Abstract:
A semi-supervised deep learning model exhibits great generalization ability with minimal required samples and has made great progress in theory and practical application over the past ten years or so. However, the lack of the model’s interpretability when modeling the internal “implicit” relationship of samples and the difficulty in constructing unsupervised regularization items have limited the further development of semi-supervised deep learning. To solve these problems and enrich the sample feature representation, this study has developed a novel semi-supervised model for image classification—semi-supervised classification model integrating the relational features (SCUTTLE). The model introduces the graph convolutional networks (GCN) based on the convolutional neural networks (CNN) and extracts the relationships between the low- and high-level features of each layer of the CNN model via the GCN model, thus extracting features and expressing relationships. By analyzing the generalization performance of the SCUTTLE model, the paper further illustrates its effectiveness in solving semi-supervised related problems. The numerical results indicate that the classification accuracy of the hybrid model with three layers of CNN and one layer of GCN can be improved by 5%–6% compared to that of the CNN model on the CIFAR10, CIFAR100, and SVHN datasets. The effectiveness of the model proposed in this paper is also proved in the most advanced fusion models of ResNet, DenseNet, WRN (wide residual networks), and GCN.
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