Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 5th International Conference on Computing and Data Science

Series Vol. 17 , 23 October 2023


Open Access | Article

Performacnes comparison of CNN-based models for fine-grained image classification

Yuxin Lu * 1 , Yang Pan 2
1 Guangxi University of Science and Technology
2 Wuhan Institute of Technology

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 17, 72-76
Published 23 October 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yuxin Lu, Yang Pan. Performacnes comparison of CNN-based models for fine-grained image classification. ACE (2023) Vol. 17: 72-76. DOI: 10.54254/2755-2721/17/20230916.

Abstract

Fine-grained visual classification is regarded as a more refined classification that can identify specific types of objects. It has been widely used in commodity sales, vehicle recognition and person recognition, etc., and has played a great value in many fields. For example, it requires an algorithm to identify different species of birds or dogs to facilitate more practical applications. This task is difficult since the objects has similar appearance and there exist obvious intra-class variance and limited inter-class differences, where different kinds of birds could share very similar appearance. Deep learning techniques has been applied to image recognition, natural language processing, and many other fields. Several approaches tackling the fine-grained classification problem are proposed. To further demonstrate the different designs of these solutions, in this paper, fine-grained identification methods are compared and analyzed, among which WS-DAN achieves better results and it is preferred to be an effective method, which is expected to be more widely used in this field.

Keywords

deep learning, image recognition, fine-grained, neural network

References

1. Zhao, B., Feng, J., Wu, X., & Yan, S. (2017). A survey on deep learning-based fine-grained object classification and semantic segmentation. International Journal of Automation and Computing, 14(2), 119-135.

2. Arslan, B., Memiş, S., Sönmez, E. B., & Batur, O. Z. (2021). Fine-grained food classification methods on the UEC food-100 database. IEEE Transactions on Artificial Intelligence, 3(2), 238-243.

3. Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., & Wang, L. (2018). Learning to navigate for fine-grained classification. In Proceedings of the European conference on computer vision (ECCV), 420-435.

4. Dubey, A., Gupta, O., Raskar, R., & Naik, N. (2018). Maximum-entropy fine grained classification. Advances in neural information processing systems, 31.

5. Hu, T., Qi, H., Huang, Q., & Lu, Y. (2019). See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv:1901.09891.

6. Fu, J., Zheng, H., & Mei, T. (2017). Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 4438-4446.

7. Zheng, H., Fu, J., Mei, T., & Luo, J. (2017). Learning multi-attention convolutional neural network for fine-grained image recognition. In Proceedings of the IEEE international conference on computer vision, 5209-5217.

8. Lin, T. Y., RoyChowdhury, A., & Maji, S. (2015). Bilinear CNN models for fine-grained visual recognition. In Proceedings of the IEEE international conference on computer vision, 1449-1457.

9. Li, P., Xie, J., Wang, Q., & Gao, Z. (2018). Towards faster training of global covariance pooling networks by iterative matrix square root normalization. In Proceedings of the IEEE conference on computer vision and pattern recognition, 947-955.

10. Rong, Y., Xu, W., Akata, Z., & Kasneci, E. (2021). Human attention in fine-grained classification. arXiv preprint arXiv:2111.01628.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
Proceedings of the 5th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-025-7
ISBN (Online)
978-1-83558-026-4
Published Date
23 October 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/17/20230916
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated