Online, self-supervised terrain classification via discriminatively trained submodular Markov random fields

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Markov processes
gradient methods
image classification
image segmentation
mobile robots
random processes
robot vision
terrain mapping
Markov random fields
autonomous terrain classification
averaged-subgradient method
classifier learning
image segmentation
inference task
max flow computation
online self-supervised terrain classification
structured prediction
submodular MRF framework
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The authors present a novel approach to the task of autonomous terrain classification based on structured prediction. We consider the problem of learning a classifier that will accurately segment an image into "obstacle" and "ground" patches based on supervised input. Previous approaches to this problem have focused mostly on local appearance; typically, a classifier is trained and evaluated on a pixel-by-pixel basis, making an implicit assumption of independence in local pixel neighborhoods. We relax this assumption by modeling correlations between pixels in the submodular MRF framework. We show how both the learning and inference tasks can be simply and efficiently implemented-exact inference via an efficient max flow computation; and learning, via an averaged-subgradient method. Unlike most comparable MRF-based approaches, our method is suitable for implementation on a robot in real-time. Experimental results are shown that demonstrate a marked increase in classification accuracy over standard methods in addition to real-time performance.

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Copyright 2008 IEEE. Reprinted from: Vernaza, P.; Taskar, B.; Lee, D.D., "Online, self-supervised terrain classification via discriminatively trained submodular Markov random fields," Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on , vol., no., pp.2750-2757, 19-23 May 2008 URL: This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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