Large Scale Metric Learning from Equivalence Constraints

Martin Köstinger, Martin Hirzer, Paul Wohlhart, Peter Roth, Horst Bischof

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. Further, if one considers the constantly growing amount of data it is often infeasible to specify fully supervised labels for all data points. Instead, it is easier to specify labels in form of equivalence constraints. We introduce a simple though effective strategy to learn a distance metric from equivalence constraints, based on a statistical inference perspective. In contrast to existing methods we do not rely on complex optimization problems requiring computationally expensive iterations. Hence, our method is orders of magnitudes faster than comparable methods. Results on a variety of challenging benchmarks with rather diverse nature demonstrate the power of our method. These include faces in unconstrained environments, matching before unseen object instances and person re-identification across spatially disjoint cameras. In the latter two benchmarks we clearly outperform the state-of-the-art.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publication statusPublished - 2012

Fields of Expertise

  • Information, Communication & Computing

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