Contextual classification with functional Max-Margin Markov Networks
Contextual classification with functional Max-Margin Markov Networks
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DOI：
10.1109/cvpr.2009.5206590
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Daniel Munoz;Andrew Bagnell
中科院分区：
文献类型：
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作者：
Daniel Munoz;Andrew Bagnell
我们解决了计算机视觉中的标签分配问题：给定一个新的3D或2D场景，我们希望为每个站点(体素、像素、超像素等)分配一个唯一的标签。为此，马尔可夫随机场框架已被证明是一种选择的模型，因为它使用上下文信息来产生比本地独立分类器更好的分类结果。在这项工作中，我们采用函数梯度方法来学习随机场的高维参数，以便执行离散的多标签分类。与以往的学习方法相比，这种方法可以更好地学习涉及高阶相互作用的稳健模型。我们在点云分类的背景下对该方法进行了验证，并改进了现有的分类方法。此外，我们还成功地展示了该方法在从图像中恢复三维几何曲面这一具有挑战性的视觉问题上的通用性。
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.