Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
Source abstract
Abstract
This paper introduces the MMTADAN, an innovative algorithm designed to enhance cross-domain image classification. By integrating multi-scale feature extraction with Taylor series-based detail enhancement and adversarial domain adaptation, the MMTADAN effectively aligns features between the source and target domains. The proposed approach addresses the critical challenge of generalizing classification models across diverse datasets, demonstrating significant improvements in performance. The findings suggest that retaining essential image details through multi-scale extraction and Taylor series enhancement can lead to better classification outcomes, making the MMTADAN a valuable contribution to the field of image classification.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and Land Use · Advanced Image and Video Retrieval Techniques · Image Enhancement Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Marine Engineering College, Dalian Maritime University, Dalian 116000, China
Department of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK
School of Mathematical Sciences, University of Nottingham, Nottingham NG9 2SE, UK
National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430078, China
Funding
National Major Scientific Research Instrument Development Project · High-Technology Ship Research Program
References (50)
Show source-provided references
1. Ding, L., Sun, Y., and Xiong, Z. (2019, January 8–12). Dual-Mode Type Algorithm for Chatter Detection in Turning Considering Beat Vibration. Proceedings of the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Hong Kong, China.10.1109/AIM.2019.8868723 ↗
2. Fang, Y., Hu, J., Shao, Q., and Qi, J. (2019, January 3–5). Fifth Order Trajectory Planning for Reducing Residual Vibration. Proceedings of the 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM), Toyonaka, Japan.10.1109/ICARM.2019.8833891 ↗
3. A Multi-Source Domain Information Fusion Network for Rotating Machinery Fault Diagnosis under Variable Operating Conditions10.1016/j.inffus.2024.102278 ↗
4. A Domain Feature Decoupling Network for Rotating Machinery Fault Diagnosis under Unseen Operating Conditions10.1016/j.ress.2024.110449 ↗
5. Self-Supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks10.1109/TPAMI.2024.3412632 ↗
6. Smooth and Time-Optimal S-Curve Trajectory Planning for Automated Robots and Machines10.1016/j.mechmachtheory.2019.03.019 ↗
7. An Approximate Maximum Likelihood Estimator for Instantaneous Frequency Estimation of Multicomponent Nonstationary Signals
8. An Approach for Jerk-Continuous Trajectory Generation of Robotic Manipulators with Kinematical Constraints10.1016/j.mechmachtheory.2020.103957 ↗
9. A Chatter Recognition Approach for Robotic Drilling System Based on Synchroextracting Chirplet Transform10.1109/JSEN.2023.3322408 ↗
10. Chatter Detection With Beat Effect Based on Beat Frequency Estimation10.1109/TASE.2020.3001623 ↗
11. Generative Adversarial Networks and Markov Random Fields for Oversampling Very Small Training Sets10.1016/j.eswa.2020.113819 ↗
12. A Comparative Analysis of Early and Late Fusion for the Multimodal Two-Class Problem10.1109/ACCESS.2023.3296098 ↗
13. Salazar, A., Safont, G., and Vergara, L. (2020, January 4–8). A New Application of Ultrasound Signal Processing for Archaeological Ceramic Classification. Proceedings of the ICASSP 2020—2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.10.1109/ICASSP40776.2020.9053400 ↗
14. Graph Regularization Methods in Soft Detector Fusion10.1109/ACCESS.2023.3344776 ↗
15. Salazar, A., Pereira, L.M., and Vergara, L. (2022, January 14–16). Experimental Study on Decision Fusion Parameters Using Alpha Integration. Proceedings of the 2022 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.10.1109/CSCI58124.2022.00015 ↗
16. Pereira, L.M., Salazar, A., and Vergara, L. (2021, January 15–17). Simultaneous Analysis of FMRI and EEG Biosignals: A Multimodal Fusion Approach. Proceedings of the 2021 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.10.1109/CSCI54926.2021.00318 ↗
17. Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey10.1016/j.inffus.2024.102601 ↗
18. Sparsity-Aware Complex-Valued Least Mean Kurtosis Algorithms10.1016/j.sigpro.2024.109637 ↗
19. Measuring Generalized Divergence for Multiple Distributions with Application to Deep Clustering10.1016/j.patcog.2024.110864 ↗
20. Frequency-Domain Diffusion Adaptation over Networks with Missing Input Data10.1016/j.sigpro.2024.109661 ↗
21. Distilling Consistent Relations for Multi-Source Domain Adaptive Person Re-Identification10.1016/j.patcog.2024.110821 ↗
22. Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis10.1016/j.patcog.2024.110912 ↗
23. Disentanglement-Inspired Single-Source Domain-Generalization Network for Cross-Scene Hyperspectral Image Classification10.1016/j.knosys.2024.112413 ↗
24. Unsupervised Domain Adaptive Building Semantic Segmentation Network by Edge-Enhanced Contrastive Learning10.1016/j.neunet.2024.106581 ↗
25. Domain Adaptive Noise Reduction with Iterative Knowledge Transfer and Style Generalization Learning10.1016/j.media.2024.103327 ↗
26. Domain Adaptation Strategies for 3D Reconstruction of the Lumbar Spine Using Real Fluoroscopy Data10.1016/j.media.2024.103322 ↗
27. Fine Classification of Crops Based on an Inductive Transfer Learning Method with Compact Polarimetric SAR Images10.1080/15481603.2024.2319939 ↗
28. Training Data in Satellite Image Classification for Land Cover Mapping: A Review10.1080/22797254.2024.2341414 ↗
29. An Improved Method for Rapid Un-Collapsed Building Extraction from Post-Disaster High-Resolution Remote Sensing Imagery Based on Multi-Scale Feature Alignment10.1080/17538947.2024.2344599 ↗
30. Enhanced Wheat Head Detection in Images Using Fourier Domain Adaptation and Random Guided Filter: Détection Améliorée Des Têtes de Blé Dans Les Images à l’aide de l’adaptation Du Domaine Fourier et Du Filtre Guidé Aléatoire10.1080/07038992.2024.2367479 ↗
31. Lowe, D.G. (1999, January 20–25). Object Recognition from Local Scale-Invariant Features. Proceedings of the Seventh IEEE International Conference on Computer Vision, Corfu, Greece.10.1109/ICCV.1999.790410 ↗
32. What and Where: A Bayesian Inference Theory of Attention10.1016/j.visres.2010.05.013 ↗
33. Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018, January 8–14). CBAM: Convolutional Block Attention Module. Proceedings of the Computer Vision—ECCV 2018, Munich, Germany.10.1007/978-3-030-01249-6 ↗
34. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7–12). Going Deeper with Convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.10.1109/CVPR.2015.7298594 ↗
35. Res2Net: A New Multi-Scale Backbone Architecture10.1109/TPAMI.2019.2938758 ↗
36. Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018, January 8–14). Image Super-Resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the Computer Vision—ECCV 2018, Munich, Germany.
37. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V. (2016). Domain-Adversarial Training of Neural Networks. arXiv.10.1007/978-3-319-58347-1_10 ↗
38. Pei, Z., Cao, Z., Long, M., and Wang, J. (2018, January 2–7). Multi-Adversarial Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.10.1609/aaai.v32i1.11767 ↗
39. Segmentation of Anatomical Structures in Chest Radiographs Using Supervised Methods: A Comparative Study on a Public Database10.1016/j.media.2005.02.002 ↗
40. Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration10.1109/TMI.2013.2290491 ↗
41. Two Public Chest X-Ray Datasets for Computer-Aided Screening of Pulmonary Diseases
42. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017, January 4–8). Attention Is All You Need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA.
43. Locality Preserving Joint Transfer for Domain Adaptation10.1109/TIP.2019.2924174 ↗
44. Long, M., Cao, Z., Wang, J., and Jordan, M.I. (2018, January 3–8). Conditional Adversarial Domain Adaptation. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montreal, QC, Canada.
45. Guided Image Filtering10.1109/TPAMI.2012.213 ↗
46. Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation10.1145/1360612.1360666 ↗
47. Gradient Domain Guided Image Filtering10.1109/TIP.2015.2468183 ↗
48. Structure-Preserving Guided Retinal Image Filtering and Its Application for Optic Disk Analysis10.1109/TMI.2018.2838550 ↗
49. Real-Time Image Smoothing via Iterative Least Squares10.1145/3388887 ↗
50. Tao, X., Zhou, C., Shen, X., Wang, J., and Jia, J. (2017, January 22–29). Zero-Order Reverse Filtering. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.33 ↗