Associative Hierarchical Random Fields
Associative Hierarchical Random Fields
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DOI：
10.1109/tpami.2013.165
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发表时间：
2014-06-01
影响因子：
23.6
通讯作者：
Torr, Philip H. S.
中科院分区：
文献类型：
--
作者：
Ladicky, L'ubor;Russell, Chris;Torr, Philip H. S.
本文的主要贡献有两个：一是提出了一种新的模型--关联分层随机场（AHRF），并给出了一种新的优化算法;二是将该模型应用于语义切分问题。语义分割的大多数方法都被公式化为可能对应于像素或片段（如超像素）的变量的标记问题。众所周知，超像素分割的生成不是唯一的。这促使许多研究人员使用多个超像素分割的问题，如语义分割或单视图重建。这些超像素尚未以原则性的方式组合，这是一个困难的问题，因为它们可能重叠，或者以分割形成分割树的方式嵌套。我们新的分层随机场模型允许来自所有多个分割的信息贡献给全局能量。在这个模型中的MAP推理可以有效地使用强大的基于图切割的移动算法。我们的框架基于像素或片段概括了以前的大部分工作，并且由此产生的标签可以被视为像素级别的详细分割，或者在另一个极端，作为一个片段选择器，它像拼图一样拼凑出一个解决方案，从不同的分割中选择最好的片段作为片段。我们评估其性能的一些最具挑战性的数据集的对象类分割，并表明，这种能力，使用多个重叠分割进行推理，导致国家的最先进的结果。
This paper makes two contributions: the first is the proposal of a new model-The associative hierarchical random field (AHRF), and a novel algorithm for its optimization; the second is the application of this model to the problem of semantic segmentation. Most methods for semantic segmentation are formulated as a labeling problem for variables that might correspond to either pixels or segments such as super-pixels. It is well known that the generation of super pixel segmentations is not unique. This has motivated many researchers to use multiple super pixel segmentations for problems such as semantic segmentation or single view reconstruction. These super-pixels have not yet been combined in a principled manner, this is a difficult problem, as they may overlap, or be nested in such a way that the segmentations form a segmentation tree. Our new hierarchical random field model allows information from all of the multiple segmentations to contribute to a global energy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms. Our framework generalizes much of the previous work based on pixels or segments, and the resulting labelings can be viewed both as a detailed segmentation at the pixel level, or at the other extreme, as a segment selector that pieces together a solution like a jigsaw, selecting the best segments from different segmentations as pieces. We evaluate its performance on some of the most challenging data sets for object class segmentation, and show that this ability to perform inference using multiple overlapping segmentations leads to state-of-the-art results.