Skip to main content
SpringerLink
Log in

Mobile phone recognition method based on bilinear convolutional neural network

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Model recognition of second-hand mobile phones has been considered as an essential process to improve the efficiency of phone recycling. However, due to the diversity of mobile phone appearances, it is difficult to realize accurate recognition. To solve this problem, a mobile phone recognition method based on bilinear-convolutional neural network (B-CNN) is proposed in this paper. First, a feature extraction model, based on B-CNN, is designed to adaptively extract local features from the images of secondhand mobile phones. Second, a joint loss function, constructed by center distance and softmax, is developed to reduce the interclass feature distance during the training process. Third, a parameter downscaling method, derived from the kernel discriminant analysis algorithm, is introduced to eliminate redundant features in B-CNN. Finally, the experimental results demonstrate that the B-CNN method can achieve higher accuracy than some existing methods.

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

Similar content being viewed by others

References

  1. Zeng X, Gong R, Chen W Q, et al. Uncovering the recycling potential of “New” WEEE in China. Environ Sci Technol, 2016, 50: 1347–1358

    Article  Google Scholar 

  2. Dias P, Machado A, Huda N, et al. Waste electric and electronic equipment (WEEE) management: A study on the Brazilian recycling routes. J Cleaner Production, 2018, 174: 7–16

    Article  Google Scholar 

  3. Parajuly K, Wenzel H. Potential for circular economy in household WEEE management. J Cleaner Production, 2017, 151: 272–285

    Article  Google Scholar 

  4. Islam M T, Huda N. Reverse logistics and closed-loop supply chain of Waste Electrical and Electronic Equipment (WEEE)/E-waste: A comprehensive literature review. Resources Conservation Recycling, 2018, 137: 48–75

    Article  Google Scholar 

  5. Zlamparet G I, Ijomah W, Miao Y, et al. Remanufacturing strategies: A solution for WEEE problem. J Cleaner Production, 2017, 149: 126–136

    Article  Google Scholar 

  6. Cao J, Chen Y, Shi B, et al. WEEE recycling in Zhejiang Province, China: Generation, treatment, and public awareness. J Cleaner Production, 2016, 127: 311–324

    Article  Google Scholar 

  7. Ueberschaar M, Geiping J, Zamzow M, et al. Assessment of element-specific recycling efficiency in WEEE pre-processing. Resources Conservation Recycling, 2017, 124: 25–41

    Article  Google Scholar 

  8. Tan Q, Dong Q, Liu L, et al. Potential recycling availability and capacity assessment on typical metals in waste mobile phones: A current research study in China. J Cleaner Production, 2017, 148: 509–517

    Article  Google Scholar 

  9. Yao L, Liu T, Chen X, et al. An integrated method of life-cycle assessment and system dynamics for waste mobile phone management and recycling in China. J Cleaner Production, 2018, 187: 852–862

    Article  Google Scholar 

  10. He W, Li G, Ma X, et al. WEEE recovery strategies and the WEEE treatment status in China. J Hazard Mater, 2006, 136: 502–512

    Article  Google Scholar 

  11. Le Q F, Zhu X, Li Y, et al. Fine-grained bird recognition by using contour-based pose transfer. Opt Eng, 2014, 26: 25–29

    Google Scholar 

  12. Zhu F, Kong X, Zheng L, et al. Part-based deep hashing for large-scale person re-identification. IEEE Trans Image Process, 2017, 26: 4806–4817

    Article  MathSciNet  Google Scholar 

  13. Wei X S, Xie C W, Wu J. Mask-CNN: Localizing parts and selecting descriptors for fine-grained image recognition. Comput Sci, 2016, 1605: 87–88

    Google Scholar 

  14. Biswas C, Mukherjee J. Logo recognition technique using sift descriptor, surf descriptor and hog descriptor. Int J Comput Appl, 2015, 117: 34–37

    Google Scholar 

  15. Satpathy A, Xudong Jiang A, How-Lung Eng A. LBP-based edge-texture features for object recognition. IEEE Trans Image Process, 2014, 23: 1953–1964

    Article  MathSciNet  Google Scholar 

  16. Lu F, Liu Y, Zhang R H. An improved hog-based vehicle logo location and recognition method. Study Opt Commun, 2012, 38: 26–29

    Google Scholar 

  17. Ye Y. Leaf classification methods based on SVM and SIFT. In: Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering. Berlin, Heidelberg: Springer, 2013, 256 (5): 341–348

    Chapter  Google Scholar 

  18. Li L, Wang C, Li W, et al. Hyperspectral image classification by AdaBoost weighted composite kernel extreme learning machines. Neurocomputing, 2018, 275: 1725–1733

    Article  Google Scholar 

  19. Ko B C, Gim J W, Nam J Y. Cell image classification based on ensemble features and random forest. Electron Lett, 2011, 47: 638–639

    Article  Google Scholar 

  20. Jiang L L, Qi Q W, Zhang A, et al. Improving the accuracy of image-based forest fire recognition and spatial positioning. Sci China Tech Sci, 2010, 53: 184–190

    Article  Google Scholar 

  21. Dong H, Ma W, Wu Y, et al. Local descriptor learning for change detection in synthetic aperture radar images via convolutional neural networks. IEEE Access, 2019, 7: 15389–15403

    Article  Google Scholar 

  22. Han J, Yao X, Cheng G, et al. P-CNN: Part-based convolutional neural networks for fine-grained visual categorization. IEEE Trans Pattern Anal Mach Intell, 2020, 1

  23. Lin D, Shen X, Lu C, et al. Deep lac: Deep localization, alignment and classification for fine-grained recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. 1666–1674

  24. Ge H, Tu X, Xie M, et al. ASP-CNN: Aligning semantic parts for finegrained image classification. J Electron Imaging, 2019, 28: 23–29

    Article  Google Scholar 

  25. Liang Y, Lu S, Weng R, et al. Unsupervised noise-robust feature extraction for aerial image classification. Sci China Tech Sci, 2020, 63: 1406–1415

    Article  Google Scholar 

  26. Agrawal P, Girshick R, Malik J. Analyzing the Performance of Multilayer Neural Networks for Object Recognition. In: Fleet D, Pajdla T, Schiele B, et al, eds. Computer Vision—ECCV 2014. Lecture Notes in Computer Science, vol 8695. Cham: Springer, 2014. 329–344

    Chapter  Google Scholar 

  27. Xie G S, Zhang X Y, Yang W, et al. LG-CNN: From local parts to global discrimination for fine-grained recognition. Pattern Recognition, 2017, 71: 118–131

    Article  Google Scholar 

  28. Hao W, Bie R, Guo J, et al. Optimized CNN based image recognition through target region selection. Int J Light Electron Opts, 2017, 156: 27–36

    Google Scholar 

  29. Lin T Y, Roychowdhury A, Maji S. Bilinear convolutional neural networks for fine-grained visual recognition. IEEE Trans Pattern Analy Machine Intell, 2018, 40: 1309–1322

    Article  Google Scholar 

  30. Krizhevsky A, Ssutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Adv Neural Infor Process Systs, 2012, 25: 1097–1105

    Google Scholar 

  31. Sochor J, Spanhel J, Herout A. Boxcars: Improving fine-grained recognition of vehicles using 3-D bounding boxes in traffic surveillance. IEEE Trans Intell Transp Syst, 2018, 20: 97–108

    Article  Google Scholar 

  32. Qi L, Lu X, Li X. Exploiting spatial relation for fine-grained image classification. Pattern Recognition, 2019, 91: 47–55

    Article  Google Scholar 

  33. Zhao J, Peng Y, He X. Attribute hierarchy based multi-task learning for fine-grained image classification. Neurocomputing, 2020, 395: 150–159

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    HongGui Han, Qi Zhen, HongYan Yang, YongPing Du & JunFei Qiao

Authors
  1. HongGui Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi Zhen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. HongYan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. YongPing Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. JunFei Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to HongGui Han.

Additional information

This work was supported by the National Key Program of China (Grant No. 2018YFC1900800-5), the National Natural Science Foundation of China (Grant Nos. 61890930-5 and 61622301), and the Beijing University Outstanding Young Scientist Program (Grant No. BJJWZYJH0120191000-5020).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, H., Zhen, Q., Yang, H. et al. Mobile phone recognition method based on bilinear convolutional neural network. Sci. China Technol. Sci. 64, 2477–2484 (2021). https://doi.org/10.1007/s11431-020-1777-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-020-1777-4

Keywords

Search

Navigation