Abstract
Fine-grained image classification (FGIC) aims to classify subordinate classes belonging to the same meta category. One of the existing FGIC methods is to use attention mechanism to localize and crop a discriminative region from the input image, and this process can be executed recurrently. In this way, the cropped image will progressively focus on a smaller local region containing the object part. However, this may cause the contour information of the object to be incomplete at the finest-scale and thereby the accuracy of the finest-scale is affected. In addition, the fusion strategy of these methods, which generally concatenates the outputs of multiple scales for the final classification, is not sufficient. To tackle the problems, based on the backbone of RA-CNN we first construct a multi-branch attention proposal network (APN) at middle scale of RA-CNN to jointly localize a most discriminative region where multiple APNs can complement each other’s incomplete contour information. Moreover, in addition to concatenating the outputs of all scales, we also use the Dempster’s combination rule to combine the outputs of all scales. Then, the features of these two parts are further combined for the final classification. Experimental results on the real-world datasets clearly validate the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chang, D., et al.: The devil is in the channels: mutual-channel loss for fine-grained image classification. IEEE Trans. Image Process. 29, 4683–4695 (2020)
Denoeux, T.: Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recogn. 30(7), 1095–1107 (1997)
Denoeux, T.: Decision-making with belief functions: a review. Int. J. Approximate Reasoning 109, 87–110 (2019)
Ding, Y., Zhou, Y., Zhu, Y., Ye, Q., Jiao, J.: Selective sparse sampling for fine-grained image recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6599–6608 (2019)
Fu, J., Zheng, H., Mei, T.: 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, pp. 4438–4446 (2017)
Li, F., Qian, Y., Wang, J., Liang, J.: multigranulation information fusion: a Dempster-Shafer evidence theory-based clustering ensemble method. Inf. Sci. 378, 389–409 (2017)
Li, S., Yao, Y., Hu, J., Liu, G., Yao, X., Hu, J.: An ensemble stacked convolutional neural network model for environmental event sound recognition. Appl. Sci. 8(7), 1152 (2018)
Shafer, G.: A Mathematical Theory of Evidence, vol. 42. Princeton University Press, Princeton (1976)
Sun, M., Yuan, Y., Zhou, F., Ding, E.: Multi-attention multi-class constraint for fine-grained image recognition. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 834–850. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_49
Wei, X.S., Wu, J., Cui, Q.: Deep learning for fine-grained image analysis: a survey. arXiv preprint arXiv:1907.03069 (2019)
Wei, X.S., Xie, C.W., Wu, J., Shen, C.: Mask-CNN: localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recogn. 76, 704–714 (2018)
Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., Wang, L.: Learning to navigate for fine-grained classification. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 438–454. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_26
Zhang, L., Huang, S., Liu, W., Tao, D.: Learning a mixture of granularity-specific experts for fine-grained categorization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8331–8340 (2019)
Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based R-CNNs for fine-grained category detection. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 834–849. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_54
Zhang, Y., et al.: Weakly supervised fine-grained categorization with part-based image representation. IEEE Trans. Image Process. 25(4), 1713–1725 (2016)
Zhao, B., Feng, J., Wu, X., Yan, S.: A survey on deep learning-based fine-grained object classification and semantic segmentation. Int. J. Autom. Comput. 14(2), 119–135 (2017). https://doi.org/10.1007/s11633-017-1053-3
Zheng, H., Fu, J., Mei, T., Luo, J.: Learning multi-attention convolutional neural network for fine-grained image recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5209–5217 (2017)
Zheng, H., Fu, J., Zha, Z.J., Luo, J., Mei, T.: Learning rich part hierarchies with progressive attention networks for fine-grained image recognition. IEEE Trans. Image Process. 29, 476–488 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, Z., Zhang, B., Fu, H., Yue, X., Lv, Y. (2021). Multi-branch Recurrent Attention Convolutional Neural Network with Evidence Theory for Fine-Grained Image Classification. In: Denœux, T., Lefèvre, E., Liu, Z., Pichon, F. (eds) Belief Functions: Theory and Applications. BELIEF 2021. Lecture Notes in Computer Science(), vol 12915. Springer, Cham. https://doi.org/10.1007/978-3-030-88601-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-88601-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88600-4
Online ISBN: 978-3-030-88601-1
eBook Packages: Computer ScienceComputer Science (R0)