Skip to main content
SpringerLink
Log in

Workpiece classification based on transfer component analysis

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Classification algorithm is the crucial technique for machine vision system. The Transfer Component Analysis has been introduced for workpiece classification system. The dataset with five classes of workpieces is constructed for experiments, and the data augmentation strategies have been employed to prevent class imbalance problems. The Histogram of the Oriented Gradient features are extracted over workpiece images, and they are transformed by the Transfer Component Analysis and the Principal Component Analysis separately. The Maximum Mean Discrepancy distances have been employed to measure the distribution distances between the training set and the testing set. And the results indicate that the Transfer Component Analysis has dramatically reduced the distribution differences in a dimensionality reduction subspace, better than the Principal Component Analysis. The Error Correction Output Code is combined with Support Vector Machine and K-Nearest Neighbor respectively as the classifiers. The macro-averaged \(F \)1 measure is adopted to evaluate the classifiers performance. Experiment results show that, the Transfer Component Analysis is particularly beneficial for improving the classification performance, and the Support Vector Machine is more favorable than the K-Nearest Neighbor for workpiece classification. The method of the Transfer Component Analysis combined with the Support Vector Machine possesses the best classification performance among these methods in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Chen, C., Abdullah, A., Kok, S. H., & Tien, D. T. K. (2022). Review of industry workpiece classification and defect detection using deep learning. International Journal of Advanced Computer Science and Applications, 13(4), 329–340.

    Article  Google Scholar 

  2. Shi, G., Zhang, Y., & Zeng, M. (2021). A fast workpiece detection method based on multi-feature fused SSD. Engineering Computations, 38(10), 3836–3852.

    Article  Google Scholar 

  3. Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2010). Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2), 199–210.

    Article  Google Scholar 

  4. Matasci, G., Volpi, M., Tuia, D., & Kanevski, M. (2011). Transfer component analysis for domain adaptation in image classification. In Image and signal processing for remote sensing XVII (Vol. 8180, pp. 125–133). SPIE.

  5. Liu, Y., Zhang, Y., Coleman, S., & Chi, J. (2018). Joint transfer component analysis and metric learning for person re-identification. Electronics Letters, 54(13), 821–823.

    Article  Google Scholar 

  6. Grubinger, T., Birlutiu, A., Schöner, H., Natschläger, T., & Heskes, T. (2015). Domain generalization based on transfer component analysis. In International work-conference on artificial neural networks (pp. 325–334). Springer, Cham.

  7. Li, X., Kong, X., Wang, B., Guo, Y., & You, X. (2013). Generalized transfer component analysis for mismatched JPEG steganalysis. In 2013 IEEE International Conference on Image Processing (pp. 4432–4436). IEEE.

  8. Hu, Z., Li, Y., Sun, H., & Ma, X. (2022). Multitasking multiobjective optimization based on transfer component analysis. Information Sciences, 605, 182–201.

    Article  Google Scholar 

  9. Li, Y., Sheng, H., Cheng, Y., Stroe, D. I., & Teodorescu, R. (2020). State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis. Applied Energy, 277, 115504.

    Article  Google Scholar 

  10. Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1–48.

    Article  Google Scholar 

  11. Van Dyk, D. A., & Meng, X. L. (2001). The art of data augmentation. Journal of Computational and Graphical Statistics, 10(1), 1–50.

    Article  MathSciNet  Google Scholar 

  12. Wang, J., & Perez, L. (2017). The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks for Visual Recognition, 11, 1–8.

    Google Scholar 

  13. Shu, C., Ding, X., & Fang, C. (2011). Histogram of the oriented gradient for face recognition. Tsinghua Science and Technology, 16(2), 216–224.

    Article  Google Scholar 

  14. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) (Vol. 1, pp. 886–893). IEEE.

  15. Tsolakidis, D. G., Kosmopoulos, D. I., & Papadourakis, G. (2014). Plant leaf recognition using Zernike moments and histogram of oriented gradients. In Hellenic conference on artificial intelligence (pp. 406–417). Springer, Cham.

  16. Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433–459.

    Article  Google Scholar 

  17. Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H. P., Schölkopf, B., & Smola, A. J. (2006). Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics, 22(14), e49–e57.

    Article  Google Scholar 

  18. Geng, X. (2020). Feature-based transfer learning. In Q. Yang, Y. Zhang, W. Y. Dai, & J. L. Pan (Eds.), Transfer learning (pp. 30–32). China Machine Press.

    Google Scholar 

  19. Gretton, A., Bousquet, O., Smola, A., & Schölkopf, B. (2005). Measuring statistical dependence with Hilbert–Schmidt norms. In International conference on algorithmic learning theory (pp. 63–77). Springer, Berlin, Heidelberg.

  20. Pan, S. J., Kwok, J. T., & Yang, Q. (2008). Transfer learning via dimensionality reduction. In AAAI (Vol. 8, pp. 677–682).

  21. Silva, S. D., Yano, M. O., & Gonsalez-Bueno, C. G. (2021). Transfer component analysis for compensation of temperature effects on the impedance-based structural health monitoring. Journal of Nondestructive Evaluation, 40(3), 1–17.

    Article  Google Scholar 

  22. Schölkopf, B., Smola, A., & Müller, K. R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.

    Article  Google Scholar 

  23. Dietterich, T. G., & Bakiri, G. (1994). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2, 263–286.

    Article  MATH  Google Scholar 

  24. Jiang, L., Cai, Z., Wang, D., & Jiang, S. (2007). Survey of improving k-nearest-neighbor for classification. In Fourth international conference on fuzzy systems and knowledge discovery (FSKD 2007) (Vol. 1, pp. 679–683). IEEE.

  25. Elsayad, A. M., Nassef, A. M., & Al-Dhaifallah, M. (2022). Bayesian optimization of multiclass SVM for efficient diagnosis of erythemato-squamous diseases. Biomedical Signal Processing and Control, 71, 103223.

    Article  Google Scholar 

  26. Agrawal, A. K., & Chakraborty, G. (2022). Semi-supervised implementation of SVM-based error-correcting output code for damage-type identification in structures. Structural Control and Health Monitoring, 29, e2967.

    Article  Google Scholar 

  27. Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2), 415–425.

    Article  Google Scholar 

  28. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

    Article  MATH  Google Scholar 

  29. Widodo, A., & Yang, B. S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21(6), 2560–2574.

    Article  Google Scholar 

  30. Demir, B., & Ertürk, S. (2010). Empirical mode decomposition of hyperspectral images for support vector machine classification. IEEE Transactions on Geoscience and Remote Sensing, 48(11), 4071–4084.

    Google Scholar 

  31. Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis. In Object-based image analysis (pp. 663–677). Springer, Berlin, Heidelberg.

  32. Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 1–13.

    Article  Google Scholar 

  33. Takahashi, K., Yamamoto, K., Kuchiba, A., & Koyama, T. (2022). Confidence interval for micro-averaged F1 and macro-averaged F1 scores. Applied Intelligence, 52(5), 4961–4972.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by Industry-University-Research Innovation Foundation of Chinese University-Blue Dot Distributed Intelligent Computing Project (Project No. 2021LDA06003); Science Foundation of Hebei Normal University (Grant No. L2021B31). Our gratitude is extended to the anonymous reviewers for their valuable comments and professional contributions to their improvement of this paper.

Author information

Authors and Affiliations

  1. HeBei Normal University, Shijiazhuang, 050024, China

    Liyong Qiao, Shuang Zhang, Chungang Liu, Huilong Jin, Hua Zhao, Lingru Cao & Yujia Ji

  2. WuHan University, Wuhan, 430072, China

    Jian Yao

Authors
  1. Liyong Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chungang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huilong Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hua Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jian Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lingru Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yujia Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huilong Jin.

Ethics declarations

Conflict interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiao, L., Zhang, S., Liu, C. et al. Workpiece classification based on transfer component analysis. Wireless Netw (2022). https://doi.org/10.1007/s11276-022-03173-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11276-022-03173-9

Keywords

Search

Navigation