Abstract
Fine-grained visual categorization (FGVC) is a challenging task due to large intra-class variance and small inter-class variance, aiming at recognizing hundreds of subcategories which belong to the same basic-level category. Humans have the ability to acquire knowledge about the world and use the acquired knowledge to reason about entities. When identifying an image, humans first reason out candidate subcategories of the object based on their prior knowledge. Then discover the characteristics of the object, which can be used as assistance for comparisons between candidate subcategories. Finally humans can determine the ultimate prediction. Inspired by this behavior, we propose a Coarse Label Refined Knowledge Reasoning (CLRKR) approach for fine-grained visual categorization. Its main novelties and advantages are as follows: (1) Knowledge Reasoning: We first construct the knowledge graph based on subcategory-attribute correlations, then reason out candidate subcategories with corresponding probabilities based on the knowledge graph. (2) Coarse Label Refinement: We incorporate coarse classes, which are pre-defined groups of attributes, to get corresponding coarse labels. These coarse labels reveal the most characteristic features of the entity in the image, which help us to re-rank the sequence of candidate subcategories and acquire the foremost subcategory as the final prediction. Our CLRKR approach achieves the best performance according to the experimental results on the widely-used CUB-200-2011 dataset for fine-grained visual categorization comparing with the state-of-the-art methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Zhang, Y., et al.: Weakly supervised fine-grained categorization with part-based image representation. IEEE Trans. Image Process. 25(4), 1713–1725 (2016)
Marino, K., Salakhutdinov, R., Gupta, A.: The more you know: using knowledge graphs for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-UCSD birds-200-2011 dataset (2011)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. In: International Conference on Learning Representations (ICLR) (2016). http://arxiv.org/abs/1511.05493
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM International Conference on Multimedia (ACM MM), pp. 675–678. ACM (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)
Yan, Y., Ni, B., Yang, X.: Fine-grained recognition via attribute-guided attentive feature aggregation. In: ACM International Conference on Multimedia (ACM MM), pp. 1032–1040. ACM (2017)
Zhou, F., Lin, Y.: Fine-grained image classification by exploring bipartite-graph labels. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1124–1133 (2016)
Fu, J., Zheng, H., Mei, T.: Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
He, X., Peng, Y., Zhao, J.: Fine-grained discriminative localization via saliency-guided faster R-CNN. In: ACM International Conference on Multimedia (ACM MM), pp. 627–635. ACM (2017)
He, X., Peng, Y.: Weakly supervised learning of part selection model with spatial constraints for fine-grained image classification. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 4075–4081 (2017)
Zhang, X., Xiong, H., Zhou, W., Tian, Q.: Fused one-vs-all features with semantic alignments for fine-grained visual categorization. IEEE Trans. Image Process. 25(2), 878–892 (2016)
Zhang, X., Xiong, H., Zhou, W., Lin, W., Tian, Q.: Picking deep filter responses for fine-grained image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1134–1142 (2016)
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Neural Information Processing Systems (NIPS), pp. 2017–2025 (2015)
Wang, D., Shen, Z., Shao, J., Zhang, W., Xue, X., Zhang, Z.: Multiple granularity descriptors for fine-grained categorization. In: International Conference on Computer Vision (ICCV), pp. 2399–2406 (2015)
Simon, M., Rodner, E.: Neural activation constellations: unsupervised part model discovery with convolutional networks. In: International Conference of Computer Vision (ICCV), pp. 1143–1151 (2015)
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: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 842–850 (2015)
Xu, Z., Tao, D., Huang, S., Zhang, Y.: Friend or Foe: fine-grained categorization with weak supervision. IEEE Trans. Image Process. (TIP) 26(1), 135–146 (2017)
Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant 61771025.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, X., Peng, Y. (2018). Coarse Label Refined Knowledge Reasoning for Fine-Grained Visual Categorization. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-02698-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02697-4
Online ISBN: 978-3-030-02698-1
eBook Packages: Computer ScienceComputer Science (R0)