Abstract
Linear metric learning is a widely used methodology to learn a dissimilarity function from a set of similar/dissimilar example pairs. Using a single metric may be a too restrictive assumption when handling heterogeneous datasets. Recently, local metric learning methods have been introduced to overcome this limitation. However, they are subjects to constraints preventing their usage in many applications. For example, they require knowledge of the class label of the training points. In this paper, we present a novel local metric learning method, which overcomes some limitations of previous approaches. The method first computes a Gaussian Mixture Model from a low dimensional embedding of training data. Then it estimates a set of local metrics by solving a convex optimization problem; finally, a dissimilarity function is obtained by aggregating the local metrics. Our experiments show that the proposed method achieves state-of-the-art results on four datasets.
Chapter PDF
Similar content being viewed by others
References
Bilenko, M., Basu, S., Mooney, R.J.: Integrating constraints and metric learning in semi-supervised clustering. In: ICML, pp. 11–18 (2004)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, New York (2004)
Cao, Q., Ying, Y., Li, P.: Similarity metric learning for face recognition. In: ICCV (2013)
Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: ICML, pp. 209–216 (2007)
Evgeniou, T., Pontil, M.: Regularized multi-task learning. In: SIGKDD, pp. 109–117 (2004)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI 32(9), 1627–1645 (2010)
Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? metric learning approaches for face identification. In: ICCV, pp. 498–505 (2009)
Hatch, A.O., Kajarekar, S., Stolcke, A.: Within-class covariance normalization for svm-based speaker recognition. In: ICSLP, pp. 1471–1474 (2006)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Tech. Rep. 07-49, University of Massachusetts, Amherst (2007)
Kan, M., Shan, S., Xu, D., Chen, X.: Side-information based linear discriminant analysis for face recognition. In: BMVC, pp. 1–12 (2011)
Köstinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: CVPR, pp. 2288–2295 (2012)
Köstinger, M., Roth, P.M., Bischof, H.: Synergy-based learning of facial identity. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds.) DAGM and OAGM 2012. LNCS, vol. 7476, pp. 195–204. Springer, Heidelberg (2012)
Maurer, A.: Learning similarity with operator-valued large-margin classifiers. JMLR 9, 1049–1082 (2008)
Nguyen, H.V., Bai, L.: Cosine similarity metric learning for face verification. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part II. LNCS, vol. 6493, pp. 709–720. Springer, Heidelberg (2011)
Noh, Y.K., Zhang, B.T., Lee, D.D.: Generative local metric learning for nearest neighbor classification. In: NIPS, pp. 1822–1830 (2010)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI 24(7), 971–987 (2002)
Parameswaran, S., Weinberger, K.Q.: Large margin multi-task metric learning. In: NIPS, pp. 1867–1875 (2010)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: CVPR, pp. 947–954 (2005)
Shen, C., Kim, J., Wang, L., van den Hengel, A.: Positive semidefinite metric learning with boosting. In: NIPS, pp. 1651–1659 (2009)
Simonyan, K., Parkhi, O.M., Vedaldi, A., Zisserman, A.: Fisher vector faces in the wild. In: BMVC (2013)
Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008), http://www.vlfeat.org/
Wang, J., Kalousis, A., Woznica, A.: Parametric local metric learning for nearest neighbor classification. In: NIPS, pp. 1610–1618 (2012)
Weinberger, K., Saul, L.: Fast solvers and efficient implementations for distance metric learning. In: ICML, pp. 1160–1167 (2008)
Weinberger, K., Saul, L.: Distance metric learning for large margin nearest neighbor classification. JMLR 10, 207–244 (2009)
Ying, Y., Li, P.: Distance metric learning with eigenvalue optimization. JMLR 13, 1–26 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bohné, J., Ying, Y., Gentric, S., Pontil, M. (2014). Large Margin Local Metric Learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8690. Springer, Cham. https://doi.org/10.1007/978-3-319-10605-2_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-10605-2_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10604-5
Online ISBN: 978-3-319-10605-2
eBook Packages: Computer ScienceComputer Science (R0)