The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We address the problem of label assignment in computer vision: given a novel 3-D or 2-D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To this end, the Markov Random Field framework has proven to be a model of choice as it uses contextual information to yield improved classification results over locally independent classifiers. In this work we adapt a functional gradient approach for learning high-dimensional parameters of random fields in order to perform discrete, multi-label classification. With this approach we can learn robust models involving high-order interactions better than the previously used learning method. We validate the approach in the context of point cloud classification and improve the state of the art. In addition, we successfully demonstrate the generality of the approach on the challenging vision problem of recovering 3-D geometric surfaces from images.

The data set is available at: http://www.cs.cmu.edu/~vmr/datasets/oakland_3d/cvpr09/doc/

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center

Associated Lab(s) / Group(s): Vision and Mobile Robotics Lab

Associated Project(s): CTA Robotics

Note: Erratum: the last line in Algorithm 2 should be: return max(0, 1+(p-|c|)/Q)

Text Reference

Daniel Munoz, J. Andrew (Drew) Bagnell, Nicolas Vandapel, and Martial Hebert, "Contextual Classification with Functional Max-Margin Markov Networks," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June, 2009.

BibTeX Reference

@inproceedings{Munoz_2009_6297,
   author = "Daniel Munoz and J. Andrew (Drew) Bagnell and Nicolas Vandapel and Martial Hebert",
   title = "Contextual Classification with Functional Max-Margin Markov Networks",
   booktitle = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)",
   month = "June",
   year = "2009",
   Notes = "Erratum: the last line in Algorithm 2 should be: return max(0, 1+(p-|c|)/Q)"
}

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