Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
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DOI：
10.1109/tpami.2009.186
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发表时间：
2010-10-01
影响因子：
23.6
通讯作者：
Bai, Xiang
中科院分区：
文献类型：
--
作者：
Tu, Zhuowen;Bai, Xiang
利用上下文信息来解决高层视觉和医学图像分割问题的概念在该领域得到了越来越多的实现。然而，如何学习一个有效和高效的上下文模型，以及一个图像外观模型，仍然是未知的。目前使用马尔可夫随机场(MRF)和条件随机场(CRF)的文献往往涉及特定的算法设计，其中建模和计算阶段是孤立地研究的。在本文中，我们提出了一种学习算法--自动上下文。给定一组训练图像及其对应的标签映射，我们首先在局部图像块上学习分类器。然后，除了原始图像块之外，使用由学习的分类器创建的区分概率(或分类置信度)图作为上下文信息，以训练新的分类器。然后，该算法迭代，直到收敛。自动上下文通过将大量的底层外观特征与上下文和隐含的形状信息融合在一起，将底层和上下文信息结合在一起。所得到的判别算法通用性强，易于实现。在训练参数几乎相同的情况下，我们将该算法应用于三种具有挑战性的视觉应用：前景/背景分离、人体形状估计和场景区域标注。此外，上下文在医学/脑图像中也起着非常重要的作用，在医学/脑图像中，解剖结构大多被限制在相对固定的位置。由于使用3D特征而不是2D特征只会产生一些微小的变化，应用于脑MRI图像分割的自动上下文算法被证明优于专门为该领域设计的最先进的算法。此外，所提出的算法的范围超出了图像分析的范围，它具有用于结构化预测问题的各种问题的潜力。
The notion of using context information for solving high-level vision and medical image segmentation problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with an image appearance model, remains mostly unknown. The current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design in which the modeling and computing stages are studied in isolation. In this paper, we propose a learning algorithm, auto-context. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. The discriminative probability (or classification confidence) maps created by the learned classifier are then used as context information, in addition to the original image patches, to train a new classifier. The algorithm then iterates until convergence. Auto-context integrates low-level and context information by fusing a large number of low-level appearance features with context and implicit shape information. The resulting discriminative algorithm is general and easy to implement. Under nearly the same parameter settings in training, we apply the algorithm to three challenging vision applications: foreground/background segregation, human body configuration estimation, and scene region labeling. Moreover, context also plays a very important role in medical/brain images where the anatomical structures are mostly constrained to relatively fixed positions. With only some slight changes resulting from using 3D instead of 2D features, the auto-context algorithm applied to brain MRI image segmentation is shown to outperform state-of-the-art algorithms specifically designed for this domain. Furthermore, the scope of the proposed algorithm goes beyond image analysis and it has the potential to be used for a wide variety of problems for structured prediction problems.