Slack and Margin Rescaling as Convex Extensions of Supermodular Functions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10746)

Abstract

Slack and margin rescaling are variants of the structured output SVM, which is frequently applied to problems in computer vision such as image segmentation, object localization, and learning parts based object models. They define convex surrogates to task specific loss functions, which, when specialized to non-additive loss functions for multi-label problems, yield extensions to increasing set functions. We demonstrate in this paper that we may use these concepts to define polynomial time convex extensions of arbitrary supermodular functions, providing an analysis framework for the tightness of these surrogates. This analysis framework shows that, while neither margin nor slack rescaling dominate the other, known bounds on supermodular functions can be used to derive extensions that dominate both of these, indicating possible directions for defining novel structured output prediction surrogates. In addition to the analysis of structured prediction loss functions, these results imply an approach to supermodular minimization in which margin rescaling is combined with non-polynomial time convex extensions to compute a sequence of LP relaxations reminiscent of a cutting plane method. This approach is applied to the problem of selecting representative exemplars from a set of images, validating our theoretical contributions.

Notes

Acknowledgments

This work is funded by Internal Funds KU Leuven, FP7-MC-CIG 334380, and the Research Foundation - Flanders (FWO) through project number G0A2716N.

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Processing Speech and Images, Departement ElektrotechniekKU LeuvenLeuvenBelgium

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