使用分层条件随机场的图像-地面分割

Jordan Reynolds, Kevin P. Murphy
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引用次数: 62

摘要

我们提出了一种检测和分割通用对象类的方法,该方法以一种新颖的方式结合了三个“现成的”组件。组件是一个通用的图像分割器,它返回一组不同尺度的“超级像素”;一个通用分类器,可以确定图像区域(如一个或多个超级像素)是否包含(部分)前景对象;以及树结构图形模型的通用信念传播(BP)过程。我们的系统将这些区域组合成一个分层的、树状结构的条件随机场,对每个节点(区域)应用分类器,并使用信念传播将所有信息融合在一起。由于我们的分类器只依赖于颜色和纹理,它们可以处理可变形(非刚性)的对象,如动物,甚至在严重的遮挡和旋转下。我们展示了在非常具有挑战性的Pascal VOC数据集上检测和分割奶牛、猫和汽车的良好结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Figure-ground segmentation using a hierarchical conditional random field
We propose an approach to the problem of detecting and segmenting generic object classes that combines three "off the shelf" components in a novel way. The components are a generic image segmenter that returns a set of "super pixels" at different scales; a generic classifier that can determine if an image region (such as one or more super pixels) contains (part of) the foreground object or not; and a generic belief propagation (BP) procedure for tree-structured graphical models. Our system combines the regions together into a hierarchical, tree-structured conditional random field, applies the classifier to each node (region), and fuses all the information together using belief propagation. Since our classifiers only rely on color and texture, they can handle deformable (non-rigid) objects such as animals, even under severe occlusion and rotation. We demonstrate good results for detecting and segmenting cows, cats and cars on the very challenging Pascal VOC dataset.
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