Cross Anisotropic Cost Volume Filtering for Segmentation
Cross Anisotropic Cost Volume Filtering for Segmentation
复制标题
用于细分的交叉各向异性成本量过滤
DOI：
10.1007/978-3-642-37331-2_60
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
J. Weickert
中科院分区：
文献类型：
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作者：
V. Kramarev;Oliver Demetz;Christopher Schroers;J. Weickert
我们研究了一种先进的监督多标签图像分割方法。为此，我们采用了一个经典的框架，最近已被Rhemann et al.（2011）振兴。它不是通常的全局能量最小化步骤，而是依赖于对每个解决方案标签的成本函数的简单评估，然后是这些成本的空间平滑步骤。虽然Rhemann等人专注于效率，但本文的目标是为通用框架配备复杂的子组件，以开发高质量的多标签图像分割方法：首先，我们提出了一种大幅改进的成本计算方案，其中包含纹理描述符以及自动特征选择策略。这导致了一个高维的特征空间，我们使用支持向量机从中提取标签成本。其次，我们提出了一种新的各向异性扩散方案的过滤步骤。在该基于PDE的过程中，成本体积的平滑被沿着先前计算的特征空间的结构操纵。广泛使用的图像数据库上的实验表明，我们的计划产生明显优越的上级质量的分割。
We study an advanced method for supervised multi-label image segmentation. To this end, we adopt a classic framework which recently has been revitalised by Rhemann et al. (2011). Instead of the usual global energy minimisation step, it relies on a mere evaluation of a cost function for every solution label, which is followed by a spatial smoothing step of these costs. While Rhemann et al. concentrate on efficiency, the goal of this paper is to equip the general framework with sophisticated subcomponents in order to develop a high-quality method for multi-label image segmentation: First, we present a substantially improved cost computation scheme which incorporates texture descriptors, as well as an automatic feature selection strategy. This leads to a high-dimensional feature space, from which we extract the label costs using a support vector machine. Second, we present a novel anisotropic diffusion scheme for the filtering step. In this PDE-based process, the smoothing of the cost volume is steered along the structures of the previously computed feature space. Experiments on widely used image databases show that our scheme produces segmentations of clearly superior quality.