NDLI: Learning a loopy model exactly

Content Provider	IEEE Xplore Digital Library
Author	Muller, Andreas Christian Behnke, Sven
Copyright Year	2014
Description	Author affiliation: Institute of Computer Science VI, Autonomous Intelligent Systems, University of Bonn, Germany (Muller, Andreas Christian; Behnke, Sven)
Abstract	Learning structured models using maximum margin techniques has become an indispensable tool for computer vision researchers, as many computer vision applications can be cast naturally as an image labeling problem. Pixel-based or superpixel-based conditional random fields are particularly popular examples. Typically, neighborhood graphs, which contain a large number of cycles, are used. As exact inference in loopy graphs is NP-hard in general, learning these models without approximations is usually deemed infeasible. In this work we show that, despite the theoretical hardness, it is possible to learn loopy models exactly in practical applications. To this end, we analyze the use of multiple approximate inference techniques together with cutting plane training of structural SVMs. We show that our proposed method yields exact solutions with an optimality guarantees in a computer vision application, for little additional computational cost. We also propose a dynamic caching scheme to accelerate training further, yielding runtimes that are comparable with approximate methods. We hope that this insight can lead to a reconsideration of the tractability of loopy models in computer vision.
Starting Page	337
Ending Page	344
File Size	1168245
Page Count	8
File Format	PDF
ISBN	9789897581335
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-05
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	SCITEPRESS
Subject Keyword	Training Computer vision Image segmentation Upper bound Computational modeling Conditional Random Fields Image Segmentation Inference algorithms Approximation methods Structured Prediction Structured SVMs
Content Type	Text
Resource Type	Article

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