Contextual classification with functional Max-Margin Markov Networks

• Published in: 2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 975 - 982
• Main Authors: Munoz, Daniel, Bagnell, J Andrew, Vandapel, Nicolas, Hebert, Martial
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2009
• Subjects: Application software; Boosting; Clouds; Computer vision; Context modeling; Learning systems; Markov random fields; Path planning; Robot vision systems; Robustness
• ISBN: 1424439922; 9781424439928
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2009.5206590

We address the problem of label assignment in computer vision: given a novel 3D or 2D 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.