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Contextual Classification with Functional Max-Margin Markov Networks

Daniel Munoz, J. Andrew (Drew) Bagnell, Nicolas Vandapel and Martial Hebert
Conference Paper, Carnegie Mellon University, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June, 2009

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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/

BibTeX Reference
@conference{Munoz-2009-10227,
title = {Contextual Classification with Functional Max-Margin Markov Networks},
author = {Daniel Munoz and J. Andrew (Drew) Bagnell and Nicolas Vandapel and Martial Hebert},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)},
notes = {Erratum: the last line in Algorithm 2 should be: return max(0, 1+(p-|c|)/Q)},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2009},
address = {Pittsburgh, PA},
}
2017-09-13T10:41:11+00:00