Large scale metric learning from equivalence constraints

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...

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Veröffentlicht in:2012 IEEE Conference on Computer Vision and Pattern Recognition S. 2288 - 2295
Hauptverfasser: Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P. M., Bischof, H.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2012
Schlagworte:
Benchmark testing
Measurement
Optimization
Scalability
Support vector machines
Training
ISBN:9781467312264, 1467312266
ISSN:1063-6919, 1063-6919
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Zusammenfassung: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.
ISBN:9781467312264
1467312266
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2012.6247939