Abstract
Fine-grained visual classification is a challenging task because of intra-class variation and inter-class similarity. Most fine-grained models predominantly focus on discriminative region localization which can effectively solve the intra-class variation, but ignore global information and the problem of inter-class similarity which easily leads to overfitting on specific samples. To address these issues, we develop an end-to-end model based on selecting discriminative features for fine-grained visual classification without the help of part or bounding box annotations. In order to accurately select discriminative features, we integrate effective information from different receptive fields to enhance the quality of features, then the features of discriminative regions detected by anchors and the whole image’s feature are jointly processed for classification. Besides, we propose a new loss function to optimize the model to find discriminative regions and prevent overfitting in the particular sample, which can simultaneously solve the problems of intra-class variation and inter-class similarity. Comprehensive experiments show that the proposed approach is superior to the state-of-the-art methods on CUB-200-2011, Stanford Cars and FGVC-Aircraft datasets.
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
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv: 1502.03167 (2015)
Li, Z., Yang, Y., Liu, X., Zhou, F., Wen, S., Xu, W.: Dynamic computational time for visual attention. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1199–1209 (2017)
Lai, D., Tian, W., Chen, L.: Improving classification with semi-supervised and fine-grained learning. Pattern Recogn. 88, 547–556 (2019)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, pp. 807–814 (2010)
Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear CNN models for fine-grained visual recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1449–1457 (2015)
Wei, X., Zhang, Y., Gong, Y., Zhang, J., Zheng, N.: Grassmann pooling as compact homogeneous bilinear pooling for fine-grained visual classification. In: Proceedings of the European Conference on Computer Vision, pp. 355–370 (2018)
Lam, M., Mahasseni, B., Todorovic, S.: Fine-grained recognition as hsnet search for informative image parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2520–2529 (2017)
Branson, S., Van Horn, G., Belongie, S., Perona, P.: Bird species categorization using pose normalized deep convolutional nets. arXiv preprint arXiv: 1406.2952 (2014)
Xiao, T., Xu, Y., Yang, K., Zhang, J., Peng, Y., Zhang, Z.: The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 842–850 (2015)
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
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)
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)
Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S.: Large scale fine-grained categorization and domain-specific transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4109–4118 (2018)
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD birds-200-2011 dataset. Computation Neural Systems Technical Report CNS-TR-2011-001, California Institute of Technology (2011)
Dubey, A., Gupta, O., Guo, P., Raskar, R., Farrell, R., Naik, N.: Pairwise confusion for fine-grained visual classification. In: Proceedings of the European Conference on Computer Vision, pp. 70–86 (2018)
Krause, J., Jin, H., Yang, J., Fei-Fei, L.: Fine-grained recognition without part annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5546–5555 (2015)
Lin, D., Shen, X., Lu, C., Jia, J.: Deep LAC: deep localization, alignment and classification for fine-grained recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1666–1674 (2015)
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 554–561 (2013)
Zhang, H., et al.: SPDA-CNN: unifying semantic part detection and abstraction for fine-grained recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1143–1152 (2016)
Simon, M., Rodner, E.: Neural activation constellations: unsupervised part model discovery with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1143–1151 (2015)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Wang, Y., Morariu, V.I., Davis, L.S.: Learning a discriminative filter bank within a CNN for fine-grained recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4148–4157 (2018)
Sun, M., Yuan, Y., Zhou, F., Ding, E.: Multi-attention multi-class constraint for fine-grained image recognition. In: Proceedings of the European Conference on Computer Vision, pp. 805–821 (2018)
Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., Wang, L.: Learning to navigate for fine-grained classification. In: Proceedings of the European Conference on Computer Vision, pp. 420–435 (2018)
Maji, S., Rahtu, E., Kannala, J., Blaschko, M., Vedaldi, A.: Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151 (2013)
Hu, T., Qi, H., Huang, Q., Lu, Y.: See better before looking closer: weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv: 1901.09891 (2019)
Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 510–519 (2019)
Singh, K.K., Lee, Y.J.: Hide-and-seek: forcing a network to be meticulous for weakly-supervised object and action localization. In: 2017 IEEE International Conference on Computer Vision, pp. 3544–3553 (2017)
Wei, X. S., Xie, C. W., Wu, J.: Mask-CNN: localizing parts and selecting descriptors for fine-grained image recognition. arXiv preprint arXiv: 1605.06878 (2016)
Wang, D., Shen, Z., Shao, J., Zhang, W., Xue, X., Zhang, Z.: Multiple granularity descriptors for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2399–2406 (2015)
Zhang, X., Xiong, H., Zhou, W., Lin, W., Tian, Q.: Picking deep filter responses for fine-grained image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1134–1142 (2016)
Cai, S., Zuo, W., Zhang, L.: Higher-order integration of hierarchical convolutional activations for fine-grained visual categorization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 511–520 (2017)
Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647–655 (2014)
Acknowledgments
The authors would like to thank the anonymous referees for their constructive comments which have helped improve the paper. This work is supported by National Natural Science Foundation of China (61502003, 71501002, 71701001, 61860206004); Natural Science Foundation of Anhui Province (1608085QF133); Key Research Project of Humanities and Social Sciences in Colleges and Universities of Anhui Province (SK2019A0013).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, Q., Li, L., Chen, Q., Luo, B. (2020). Selecting Discriminative Features for Fine-Grained Visual Classification. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_65
Download citation
DOI: https://doi.org/10.1007/978-3-030-41404-7_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41403-0
Online ISBN: 978-3-030-41404-7
eBook Packages: Computer ScienceComputer Science (R0)