Abstract
In recent years, the learned local descriptors have outperformed handcrafted ones by a large margin, due to the powerful deep convolutional neural network architectures such as L2-Net [1] and triplet based metric learning [2]. However, there are two problems in the current methods, which hinders the overall performance. Firstly, the widely-used margin loss is sensitive to incorrect correspondences, which are prevalent in the existing local descriptor learning datasets. Second, the L2 distance ignores the fact that the feature vectors have been normalized to unit norm. To tackle these two problems and further boost the performance, we propose a robust angular loss which (1) uses cosine similarity instead of L2 distance to compare descriptors and (2) relies on a robust loss function that gives smaller penalty to triplets with negative relative similarity. The resulting descriptor shows robustness on different datasets, reaching the state-of-the-art result on Brown dataset, as well as demonstrating excellent generalization ability on the Hpatches dataset and a Wide Baseline Stereo dataset.
Supported by grant Pfizer and organization by SAP SE and CNRS INS2IJCJC-INVISANA.
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
Similar content being viewed by others
References
Tian, Y., Fan, B., Wu, F.: L2-net: deep learning of discriminative patch descriptor in euclidean space. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6128–6136 (2017)
Mishchuk, A., Mishkin, D., Radenovic, F., Matas, J.: Working hard to know your neighbor’s margins: local descriptor learning loss. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 4829–4840. Curran Associates, Inc. (2017)
Choy, C.B., Gwak, J., Savarese, S., Chandraker, M.: Universal correspondence network. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 2414–2422. Curran Associates, Inc. (2016)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Fischer, P., Dosovitskiy, A., Brox, T.: Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT. ArXiv e-prints (2014)
Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.: Discriminative learning of deep convolutional feature point descriptors. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 118–126 (2015)
Han, X., Leung, T., Jia, Y., Sukthankar, R., Berg, A.C.: Matchnet: unifying feature and metric learning for patch-based matching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3279–3286 (2015)
Zagoruyko, S., Komodakis, N.: Learning to Compare Image Patches via Convolutional Neural Networks. ArXiv e-prints (2015)
Simonyan, K., Vedaldi, A., Zisserman, A.: Learning local feature descriptors using convex optimisation. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1573–1585 (2014)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18, 509–517 (1975)
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823 (2015)
Brown, M., Hua, G., Winder, S.: Discriminative learning of local image descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 33, 43–57 (2011)
Tola, E., Lepetit, V., Fua, P.: A fast local descriptor for dense matching (2008)
Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors, pp. 506–513 (2004)
Balntas, V., Tang, L., Mikolajczyk, K.: Bold - binary online learned descriptor for efficient image matching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2367–2375 (2015)
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, pp. 487–495. Curran Associates, Inc. (2014)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898 (2014)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1735–1742 (2006)
Varior, R.R., Haloi, M., Wang, G.: Gated siamese convolutional neural network architecture for human re-identification. CoRR abs/1607.08378 (2016)
Lin, J., Morère, O., Chandrasekhar, V., Veillard, A., Goh, H.: Deephash: getting regularization, depth and fine-tuning right. CoRR abs/1501.04711 (2015)
Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. J. Mach. Learn. Res. 11, 1109–1135 (2010)
Ustinova, E., Lempitsky, V.: Learning deep embeddings with histogram loss. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 4170–4178. Curran Associates, Inc. (2016)
Yu, Y., Yang, M., Xu, L., White, M., Schuurmans, D.: Relaxed clipping: a global training method for robust regression and classification. In: NIPS (2010)
Wu, C., Manmatha, R., Smola, A.J., Krähenbühl, P.: Sampling matters in deep embedding learning. CoRR abs/1706.07567 (2017)
Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition (2018)
Balntas, V., Lenc, K., Vedaldi, A., Mikolajczyk, K.: HPatches: a benchmark and evaluation of handcrafted and learned local descriptors. CoRR abs/1704.05939 (2017)
Mishkin, D., Matas, J., Perdoch, M., Lenc, K.: WxBS: wide baseline stereo generalizations. CoRR abs/1504.06603 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, Y., Gong, M., Liu, T., Batmanghelich, K., Wang, C. (2019). Robust Angular Local Descriptor Learning. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-20873-8_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20872-1
Online ISBN: 978-3-030-20873-8
eBook Packages: Computer ScienceComputer Science (R0)