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Associative Hierarchical Random Fields

机译:关联分层随机字段

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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.
机译:本文做出了两点贡献:首先是提出了一种新模型-关联层次随机场(AHRF),以及一种对其进行优化的新算法。第二是该模型在语义分割问题上的应用。大多数用于语义分割的方法被表述为可能对应于像素或片段(例如超像素)的变量的标记问题。众所周知,超级像素分割的生成不是唯一的。这促使许多研究人员将多个超像素分割用于诸如语义分割或单视图重构之类的问题。这些超像素还没有以原则性的方式进行组合,这是一个难题,因为它们可能重叠或以分割形成分割树的方式嵌套。我们新的分层随机字段模型允许来自所有多个细分的信息为全球能源做出贡献。使用强大的基于图割的移动制作算法,可以有效地执行此模型中的MAP推断。我们的框架会根据像素或细分来概括先前的大部分工作,而生成的标签既可以看作是像素级别的详细细分,也可以看作是细分选择器,可以像拼图一样拼凑解决方案,从不同细分中选择最佳细分。我们在一些最具挑战性的数据集上进行了评估,以进行对象类细分,并证明了使用多个重叠细分进行推理的能力导致了最新的结果。

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