Abstract
Fine-grained search task such as retrieving subordinate categories of birds, dogs or cars, has been an important but challenging problem in computer vision. Although many effective fine-grained search methods were developed, with the amount of data increasing, previous methods fail to handle the explosive fine-grained data with low storage cost and fast query speed. On the other side, since hashing sheds its light in large-scale image search for dramatically reducing the storage cost and achieving a constant or sub-linear time complexity, we leverage the power of hashing techniques to tackle this valuable yet challenging vision task, termed as fine-grained hashing in this paper. Specifically, our proposed method consists of two crucial modules, i.e., the bilinear feature learning and the binary hash code learning. While the former encodes both local and global discriminative information of a fine-grained image, the latter drives the whole network to learn the final binary hash code to present that fine-grained image. Furthermore, we also introduce a novel multi-task hash training strategy, which can learn hash codes of different lengths simultaneously. It not only accelerates training procedures, but also significantly improves the fine-grained search accuracy. By conducting comprehensive experiments on diverse fine-grained datasets, we validate that the proposed method achieves superior performance over the competing baselines.
Y. Wang—Is a student.
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References
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 51(1), 117 (2008)
Andoni, A., Razenshteyn, I.: Optimal data-dependent hashing for approximate near neighbors. In: STOC, pp. 793–801 (2015)
Bossard, L., Guillaumin, M., Van Gool, L.: Food-101 – mining discriminative components with random forests. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 446–461. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_29
Cao, Z., Long, M., Wang, J., Yu, P.S.: HashNet: deep learning to hash by continuation. In: ICCV, pp. 5608–5617 (2017)
Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Fei-Fei, L.: Fine-grained car detection for visual census estimation. In: AAAI, pp. 4502–4508 (2017)
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: VLDB, pp. 518–529 (1999)
Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. TPAMI 35(12), 2916–2929 (2012)
Horn, G.V., Branson, S., Farrell, R., Haber, S.: Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. In: CVPR, pp. 595–604 (2015)
Hou, S., Feng, Y., Wang, Z.: VegFru: a domain-specific dataset for fine-grained visual categorization. In: ICCV, pp. 541–549 (2017)
Jiang, Q.Y., Li, W.J.: Asymmetric deep supervised hashing. In: AAAI, pp. 3342–3349 (2018)
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NeurIPS, pp. 1097–1105 (2012)
Li, W.J., Wang, S., Kang, W.C.: Feature learning based deep supervised hashing with pairwise labels. arXiv preprint arXiv:1511.03855 (2015)
Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear convolutional neural networks for fine-grained visual recognition. TPAMI 40(6), 1309–1322 (2018)
Liu, H., Wang, R., Shan, S., Chen, X.: Deep supervised hashing for fast image retrieval. In: CVPR, pp. 2064–2072 (2016)
Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: CVPR, pp. 2074–2081 (2012)
Maji, S., Kannala, J., Rahtu, E., Blaschko, M., Vedaldi, A.: Fine-grained visual classification of aircraft. Technical report (2013)
Mu, Y., Wright, J., Chang, S.-F.: Accelerated large scale optimization by concomitant hashing. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 414–427. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33718-5_30
Shen, F., Shen, C., Liu, W., Tao Shen, H.: Supervised discrete hashing. In: CVPR, pp. 37–45 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Wah, C., Branson, S., Welinder, P., Pietro Perona, S.B.: The Caltech-UCSD Birds-200-2011 Dataset. Technical report CNS-TR-2011-001, California Institute of Technology (2011)
Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval. In: CVPR, pp. 3424–3431 (2010)
Wang, Y., Song, R., Wei, X.S., Zhang, L.: An adversarial domain adaptation network for cross-domain fine-grained recognition. In: WACV, pp. 1228–1236 (2020)
Wei, X.S., Cui, Q., Yang, L., Wang, P., Liu, L.: RPC: a large-scale retail product checkout dataset. arXiv preprint arXiv:1901.07249 (2019)
Wei, X.S., Luo, J.H., Wu, J., Zhou, Z.H.: Selective convolutional descriptor aggregation for fine-grained image retrieval. TIP 26(6), 2868–2881 (2017)
Wei, X.S., Wu, J., Cui, Q.: Deep learning for fine-grained image analysis: a survey. arXiv preprint arXiv:1907.03069 (2019)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NeurIPS, pp. 1753–1760 (2009)
Xie, L., Wang, J., Zhang, B., Tian, Q.: Fine-grained image search. TMM 17(5), 636–647 (2015)
Zheng, X., Ji, R., Sun, X., Wu, Y., Huang, F., Yang, Y.: Centralized ranking loss with weakly supervised localization for fine-grained object retrieval. In: IJCAI, pp. 1226–1233 (2018)
Zheng, X., Ji, R., Sun, X., Zhang, B., Wu, Y., Huang, F.: Towards optimal fine grained retrieval via decorrelated centralized loss with normalize-scale layer. In: AAAI, pp. 9291–9298 (2019)
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Wang, Y., Wei, XS., Xue, B., Zhang, L. (2020). Piecewise Hashing: A Deep Hashing Method for Large-Scale Fine-Grained Search. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_36
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