基于对比自监督学习的图像分类框架

doi:10.13229/j.cnki.jdxbgxb20210607

Abstract

Abstract:

In order to solve the problem that supervised learning needs a lot of time to complete data set annotation in the field of image classification， a self-supervised image classification framework， SSIC framework， is proposed. SSIC framework is a self supervised learning method based on contrastive learning， which has better performance than the existing unsupervised methods. A new framework is designed and a more effective pretext task is selected to improve the robustness of the model. In addition， a targeted loss function is proposed to improve the performance of image classification. experiments was conducted on UC Merced， NWPU and AID data sets. Experimental results show that SSIC framework has obvious advantages over the latest technology， and it also performs well in low resolution image classification.

Key words: computer application, self-supervised learning, contrastive learning, image classification

CLC Number:

TP391

Hong-wei ZHAO,Jian-rong ZHANG,Jun-ping ZHU,Hai LI. Image classification framework based on contrastive self⁃supervised learning[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1850-1856.

Figures/Tables 7

Fig.1

Fig.2

Fig.3

Table 1

Table 2

Fig.4

Table 3

References 17

1	Lowe D G. Distinctive image features from scale-invariant key points[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
2	赵宏伟, 霍东升, 王洁, 等. 基于显著性检测的害虫图像分类[J]. 吉林大学学报: 工学版, 2021, 51(6): 2174-2181.
	Zhao Hong-wei, Huo Dong-sheng, Wang Jie,et al. Image classification of insect pests based on saliency detection[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2174-2181.
3	许骞艺, 秦贵和, 孙铭会, 等.基于改进的ResNeSt驾驶员头部状态分类算法[J].吉林大学学报: 工学版, 2021, 51(2): 704-711.
	Xu Qian-yi, Qin Gui-he, Sun Ming-hui, et al. Classification of drivers' head status based on improved ResNeSt[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 704-711.
4	Yu Y, Li X, Liu F. Attention GANs: unsupervised deep feature learning for aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1): 519-531.
5	Lin D, Fu K, Wang Y, et al. MARTA GANs: unsupervised representation learning for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 14(11): 2092-2096.
6	Geoffrey E H, Simon O, Yee-Whye T. A fast-learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.
7	Diederik P K, Max W. Auto-encoding variational bayes[J]. arXiv Preprint arXiv:.
8	Chen T, Kornblith S, Norouzi M, et al. A simple framework for contrastive learning of visual representations[C]∥In Proceedings of the International Conference on Machine Learning, Australia, 2020: 10709-10719.
9	He K, Fan H, Wu Y, et al. Momentum contrast for unsupervised visual representation learning[C]∥In Proceedings of the Conference on Computer Vision and Pattern Recognition, Los Alamitos, 2020: 9729-9738.
10	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016: 770-778.
11	Wu Z, Xiong Y, Yu S, et al. Unsupervised feature learning via non-parametric instance discrimination[C]∥In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos, 2018: 3733-3742.
12	Yang Y, Newsam S. Bag-of-visual-words and spatial extensions for land-use classification[C]∥Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, 2010: 270-279.
13	Cheng G, Han J, Lu X. Remote sensing image scene classification: benchmark and state of the art[J]. Proceedings of the IEEE, 2017, 105(10): 1865-1883.
14	Xia G S. AID: a benchmark data set for performance evaluation of aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7): 3965-3981.
15	Qi B, Kun Q, Zhang H, et al. APDC-Net: attention pooling-based convolutional network for aerial scene classification[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 15(10): 1603-1607.
16	Sun H, Li S, Zheng X, et al. Remote sensing scene classification by gated bidirectional network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1): 82-96.
17	Tan M, Le Q V. EfficientNet: rethinking model scaling for convolutional neural networks[C]∥In International Conference on Machine Learning, Long Beach, 2019: 10691-10700.

Related Articles 10

[1]	Huai-jiang YANG,Er-shuai WANG,Yong-xin SUI,Feng YAN,Yue ZHOU. Simplified residual structure and fast deep residual networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1413-1421.
[2]	Xiang-jun LI,Jie-ying TU,Zhi-bin ZHAO. Validity classification of melting curve based on multi⁃scale fusion convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 633-639.
[3]	Liang DUAN,Chun-yuan SONG,Chao LIU,Wei WEI,Cheng-ji LYU. State recognition in bearing temperature of high-speed train based on machine learning algorithms [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 53-62.
[4]	Qian-yi XU,Gui-he QIN,Ming-hui SUN,Cheng-xun MENG. Classification of drivers' head status based on improved ResNeSt [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 704-711.
[5]	Xiang-jiu CHE,Hua-luo LIU,Qing-bin SHAO. Fabric defect recognition algorithm based onimproved Fast RCNN [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2038-2044.
[6]	CHEN Mian-shu, SU Yue, SANG Ai-jun, LI Pei-peng. Image classification methods based on space vector model [J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[7]	CHEN Zai-qing, SHI Jun-sheng, BAI Feng-xiang. Automatic image classification based on fuzzy-rough set [J]. 吉林大学学报(工学版), 2013, 43(增刊1): 209-212.
[8]	WANG Ying, GUO Lei, LIANG Nan. Classification algorithm of hyperspectral images based on kernel entropy analysis [J]. , 2012, (06): 1597-1601.
[9]	LIU Ping-ping, ZHAO Hong-wei, GENG Qing-tian, DAI Jin-bo. Image classification method based on local feature and visual cortex recognition mechanism [J]. 吉林大学学报(工学版), 2011, 41(05): 1401-1406.
[10]	Cao Chun-hong Zhang Bin,Li Xiao-lin . Medical image classification technology based on fuzzy support vector machine [J]. 吉林大学学报(工学版), 2007, 37(03): 630-0633.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

方法	50%训练数据		80%训练数据
方法	Top1	Top5	Top1	Top5
NT-Xent^［8］	89.72	98.47	94.31	99.47
InfoNCE^［9］	84.38	95.85	93.98	98.83
Dot product for similarity	82.37	95.43	92.90	98.91
L2 distance for similarity	90.58	98.89	94.40	99.94
MultiNC loss without noise	94.35	99.94	97.85	99.97
MultiNC loss（λ=1）	96.19	100.00	98.54	100.00
MultiNC loss（λ=0.5）	96.21	100.00	98.96	100.00

方法		UC-Merced		AID		NWPU
方法		50%训练数据	80%训练数据	20%训练数据	50%训练数据	10%训练数据	20%训练数据
有监督学习	VGGNet^［14］	94.14 ± 0.69	95.21 ± 1.20	86.59 ± 0.29	89.64 ± 0.36	76.47 ± 0.18	79.79 ± 0.65
	GoogleNet^［14］	92.70 ± 0.60	94.31 ± 0.89	83.44 ± 0.40	86.39 ± 0.55	76.19 ± 0.38	78.48 ± 0.26
	SPPNet^［14］	94.77 ± 0.46	96.67 ± 0.94	87.44 ± 0.45	91.45 ± 0.38	82.13 ± 0.30	84.64 ± 0.23
	APDCNet^［15］	95.01 ± 0.43	97.05 ± 0.43	88.65 ± 0.29	92.15 ± 0.29	85.94 ± 0.22	87.84 ± 0.26
	GBNet^［16］	95.71 ± 0.19	96.90 ± 0.23	90.16 ± 0.24	93.70 ± 0.34	-	-
	GBNet+Global feature^［16］	97.05 ± 0.19	98.57 ± 0.48	92.20 ± 0.23	95.48 ± 0.12	-	-
无监督学习	LLC^［14］	70.12 ± 1.09	72.55 ± 1.83	58.06 ± 0.50	63.24 ± 0.44	38.81 ± 0.23	40.03 ± 0.34
	BoVW^［14］	72.40 ± 1.30	75.52 ± 2.13	62.49 ± 0.53	68.37 ± 0.40	41.72 ± 0.21	44.97 ± 0.28
	MATAR GAN^［5］	85.51 ± 0.69	94.86 ± 0.80	75.39 ± 0.49	81.57 ± 0.33	68.63 ± 0.22	75.03 ± 0.28
	Attention GAN^［4］	89.06 ± 0.50	97.69 ± 0.69	78.95 ± 0.23	84.52 ± 0.18	72.21 ± 0.21	77.99 ± 0.19
本文	SSIC Framework	96.21 ± 0.31	98.96 ± 0.22	92.27 ± 0.48	95.82 ± 0.19	87.61 ± 0.34	90.04 ± 0.24

方法	50%训练数据
方法	224×224	64×64	32×32
ResNet18^［10］	93.88	83.65	73.90
ResNet34^［10］	94.67	85.56	74.37
ResNet50^［10］	94.78	86.23	76.93
EfficientNetB4^［17］	93.51	81.49	71.21
EfficientNetB7^［17］	94.83	87.59	74.88
SSIC（本文）	96.21	91.13	83.02

Image classification framework based on contrastive self⁃supervised learning

RICH HTML

PDF (PC)