Multi-label infrared image classification algorithm based on weakly supervised learning

MIAO Chuankai; LOU Shuli; LI Ting; CAI Huimin

doi:10.37188/OPE.20223020.2501

您当前的位置：

首页 >

文章列表页 >

Multi-label infrared image classification algorithm based on weakly supervised learning

Information Sciences | 更新时间：2026-09-12

- Multi-label infrared image classification algorithm based on weakly supervised learning
- Optics and Precision Engineering Vol. 30, Issue 20, Pages: 2501-2509(2022)
- 作者机构：
  
  1.烟台大学物理与电子信息学院，山东烟台 264005
  2.天津津航技术物理研究所，天津 300308
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223020.2501
  CLC： TN219;TP391.4
- Received：09 May 2022，
  
  Revised：2022-06-17，
  
  Published：25 October 2022
- 稿件说明：
移动端阅览
苗传开,娄树理,李婷等.基于弱监督学习的多标签红外图像分类算法[J].光学精密工程,2022,30(20):2501-2509.

MIAO Chuankai,LOU Shuli,LI Ting,et al.Multi-label infrared image classification algorithm based on weakly supervised learning[J].Optics and Precision Engineering,2022,30(20):2501-2509.
苗传开,娄树理,李婷等.基于弱监督学习的多标签红外图像分类算法[J].光学精密工程,2022,30(20):2501-2509. DOI： 10.37188/OPE.20223020.2501.

MIAO Chuankai,LOU Shuli,LI Ting,et al.Multi-label infrared image classification algorithm based on weakly supervised learning[J].Optics and Precision Engineering,2022,30(20):2501-2509. DOI： 10.37188/OPE.20223020.2501.

摘要

红外图像的场景感知与分类分级是图像识别的一项关键技术，对于红外侦察与制导具有重要意义。为有效解决红外图像多场景多目标的场景感知及分类分级的问题，本文提出一种基于弱监督学习的多标签红外图像分类算法。将多标签图像分类技术应用于红外前视图像领域，针对多场景的红外图像进行弱监督的图像级标注，使用主干网络Resnet-50对图像进行特征提取；引入类特定的空间残差注意力模块CSRA以捕捉图像中不同类别所占据的不同空间区域，提高类别特征的表达性能；引入先进的损失函数ASL以解决多标签分类中正负标签数量失衡问题，使训练过程中更多地关注阳性样本，提高检测准确率。试验结果表明，本文算法对于多场景多目标的红外图像分类具有更好的适应性和准确率，算法检测率可达90%以上，能够很好地完成红外图像分类分级任务。

Abstract

Scene perception and classification of FLIR images is a key technology in target recognition and of great significance to infrared reconnaissance and guidance. To resolve the problem of scene perception and classification of FLIR images， this study proposes a multi-label infrared image classification algorithm based on weakly supervised learning. First， a multi-label image classification technique is applied to FLIR images， and the images of multiple scenes are annotated using weakly supervised techniques. Infrared image features are extracted using the ResNet-50 network with a residual structure. Second， a CSRA module is introduced to capture the different spatial regions occupied by different classes. The CSRA module can improve the feature expression performance and realize the inference calculation of topological relationships between multiple labels. Finally， the advanced loss function ASL is introduced to solve the imbalance of the number of positive and negative labels in multi-label classification. The advanced loss limits the contribution of negative samples to the loss function and focuses attention on the positive samples during training. An experiment shows that the algorithm has good adaptability and accuracy， and the accuracy can exceed 90%. The algorithm can be used to perform multi-label classification with high accuracy and adaptability.

关键词

Keywords

references

WU J X ， YANG H . Linear regression-based efficient SVM learning for large-scale classification ［J］. IEEE Transactions on Neural Networks and Learning Systems ， 2015 ， 26 （ 10 ）： 2357 - 2369 . doi: 10.1109/tnnls.2014.2382123 http://dx.doi.org/10.1109/tnnls.2014.2382123

林春焕 . 弱监督学习下的多标签图像分类［D］. 西安：西安电子科技大学， 2019 .

LIN C H . Multi-label Image Classification Under Weakly Supervised Learning ［D］. Xi'an ： Xidian University ， 2019 . （in Chinese）

SERMANET P ， EIGEN D ， ZHANG X ， et al . Overfeat： Integrated recognition， localization and detection using convolutional networks . arXiv preprint arXiv： 1312.6229 ， 2013 .

WEI Y C ， XIA W ， LIN M ， et al . HCP： a flexible CNN framework for multi-label image classification ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence ， 2016 ， 38 （ 9 ）： 1901 - 1907 . doi: 10.1109/tpami.2015.2491929 http://dx.doi.org/10.1109/tpami.2015.2491929

WANG J ， YANG Y ， MAO J H ， et al . CNN-RNN： a unified framework for multi-label image classification ［C］. 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas， NV， USA . IEEE ， 2016 ： 2285 - 2294 . doi: 10.1109/cvpr.2016.251 http://dx.doi.org/10.1109/cvpr.2016.251

CHEN T S ， XU M X ， HUI X L ， et al . Learning semantic-specific graph representation for multi-label image recognition ［C］. 2019 IEEE/CVF International Conference on Computer Vision （ICCV）. Seoul ， Korea （South） . IEEE ， 2019 ： 522 - 531 . doi: 10.1109/iccv.2019.00061 http://dx.doi.org/10.1109/iccv.2019.00061

LANCHANTIN J ， WANG T L ， ORDONEZ V ， et al . General multi-label image classification with transformers ［C］. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Nashville ， TN ， USA . IEEE ， 2021 ： 16473 - 16483 . doi: 10.1109/cvpr46437.2021.01621 http://dx.doi.org/10.1109/cvpr46437.2021.01621

CHENG X ， LIN H Z ， WU X Y ， et al . MLTR： multi-label classification with transformer ［C］. 2022 IEEE International Conference on Multimedia and Expo. Taipei ， Taiwan， China . IEEE ， 2022 ： 1 - 6 . doi: 10.1109/icme52920.2022.9860016 http://dx.doi.org/10.1109/icme52920.2022.9860016

LIU S ， ZHANG L ， YANG X ， et al . Query2Label： A Simple Transformer Way to Multi-Label Classification ［J］. arXiv preprint arXiv： 2107.10834 ， 2021 .

HE K M ， ZHANG X Y ， REN S Q ， et al . Deep residual learning for image recognition ［C］. 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas， NV， USA . IEEE ， 2016 ： 770 - 778 . doi: 10.1109/cvpr.2016.90 http://dx.doi.org/10.1109/cvpr.2016.90

ZHU K ， WU J X . Residual attention： a simple but effective method for multi-label recognition ［C］. 2021 IEEE/CVF International Conference on Computer Vision （ICCV）. Montreal ， QC， Canada . IEEE ， 2021 ： 184 - 193 . doi: 10.1109/iccv48922.2021.00025 http://dx.doi.org/10.1109/iccv48922.2021.00025

CUI Y ， JIA M L ， LIN T Y ， et al . Class-balanced loss based on effective number of samples ［C］. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Long Beach ， CA， USA . IEEE ， 2019 ： 9260 - 9269 . doi: 10.1109/cvpr.2019.00949 http://dx.doi.org/10.1109/cvpr.2019.00949

WU T ， HUANG Q Q ， LIU Z W ， et al . Distribution-balanced Loss for Multi-label Classification in Long-tailed Datasets ［M］. Computer Vision – ECCV 2020 . Cham ： Springer International Publishing ， 2020 ： 162 - 178 . doi: 10.1007/978-3-030-58548-8_10 http://dx.doi.org/10.1007/978-3-030-58548-8_10

RIDNIK T ， BEN-BARUCH E ， ZAMIR N ， et al . Asymmetric loss for multi-label classification ［C］. 2021 IEEE/CVF International Conference on Computer Vision （ICCV）. Montreal ， QC， Canada . IEEE ， 2021 ： 82 - 91 . doi: 10.1109/iccv48922.2021.00015 http://dx.doi.org/10.1109/iccv48922.2021.00015

YE J ， HE J J ， PENG X J ， et al . Attention-driven Dynamic Graph Convolutional Network for Multi-label Image Recognition ［M］. Computer Vision – ECCV 2020 . Cham ： Springer International Publishing ， 2020 ： 649 - 665 . doi: 10.1007/978-3-030-58589-1_39 http://dx.doi.org/10.1007/978-3-030-58589-1_39

YOU R C ， GUO Z Y ， CUI L ， et al . Cross-modality attention with semantic graph embedding for multi-label classification ［J］. Proceedings of the AAAI Conference on Artificial Intelligence ， 2020 ， 34 （ 7 ）： 12709 - 12716 . doi: 10.1609/aaai.v34i07.6964 http://dx.doi.org/10.1609/aaai.v34i07.6964

LIN T Y ， GOYAL P ， GIRSHICK R ， et al . Focal loss for dense object detection ［C］. 2017 IEEE International Conference on Computer Vision . Venice， Italy . IEEE ， 2017 ： 2999 - 3007 . doi: 10.1109/iccv.2017.324 http://dx.doi.org/10.1109/iccv.2017.324

Views

1284

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Semantically controllable fusion of infrared and visible images

Weakly supervised image semantic segmentation network driven by spatio-temporal contrastive learning

Infrared and visible image fusion based on target enhancement and butterfly optimization

Image fusion of dual-discriminator generative adversarial network and latent low-rank representation

Infrared dim-small target detection based on an improved multiscale fractal feature

Related Author

LIU Changyuan

CHENG Bingyun

WU Haibin

LIANG Zhen

HU Yanzhu

YANG Yang

HAO Shuai

LI Tong

Related Institution

College of Measurement-Control Technology and Communication Engineering， Harbin University of Science and Technology

Key Laboratory of IoT Monitoring and Early Warning， Ministry of Emergency Management， Beijing University of Posts and Telecommunications

School of Intelligent Engineering and Automation， Beijing University of Posts and Telecommunications

College of Electrical and Control Engineering， Xi'an University of Science and Technology

AECC Shenyang Liming Aero-Engine Co.， LTD.

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰