Telecom Bretagne, UMR CNRS, Brest, France.
IEEE Trans Image Process. 2010 Dec;19(12):3146-56. doi: 10.1109/TIP.2010.2071290. Epub 2010 Sep 2.
This paper investigates variational region-level criterion for supervised and unsupervised texture-based image segmentation. The focus is given to the demonstration of the effectiveness and robustness of this region-based formulation compared to most common variational approaches. The main contributions of this global criterion are twofold. First, the proposed methods circumvent a major problem related to classical texture based segmentation approaches. Existing methods, even if they use different and various texture features, are mainly stated as the optimization of a criterion evaluating punctual pixel likelihoods or similarity measure computed within a local neighborhood. These approaches require sufficient dissimilarity between the considered texture features. An additional limitation is the choice of the neighborhood size and shape. These two parameters and especially the neighborhood size significantly influence the classification performances: the neighborhood must be large enough to capture texture structures and small enough to guarantee segmentation accuracy. These parameters are often set experimentally. These limitations are mitigated with the proposed variational methods stated at the region-level. It resorts to an energy criterion defined on image where regions are characterized by nonparametric distributions of their responses to a set of filters. In the supervised case, the segmentation algorithm consists in the minimization of a similarity measure between region-level statistics and texture prototypes and a boundary based functional that imposes smoothness and regularity on region boundaries. In the unsupervised case, the data-driven term involves the maximization of the dissimilarity between regions. The proposed similarity measure is generic and permits optimally fusing various types of texture features. It is defined as a weighted sum of Kullback-Leibler divergences between feature distributions. The optimization of the proposed variational criteria is carried out using a level-set formulation. The effectiveness and the robustness of this formulation at region-level, compared to classical active contour methods, are evaluated for various Brodatz and natural images.
本文研究了监督和无监督纹理图像分割的变分区域准则。重点在于展示这种基于区域的公式与最常见的变分方法相比的有效性和鲁棒性。该全局准则的主要贡献有两点。首先，所提出的方法规避了与经典纹理分割方法相关的一个主要问题。即使使用不同和各种纹理特征，现有方法主要是优化评估点状像素似然度的准则或在局部邻域内计算的相似性度量的优化。这些方法要求所考虑的纹理特征之间有足够的差异。另一个限制是邻域大小和形状的选择。这两个参数，特别是邻域大小，对分类性能有显著影响：邻域必须足够大以捕获纹理结构，并且足够小以保证分割精度。这些参数通常通过实验来设置。这些限制在基于区域的所提出的变分方法中得到缓解。它依赖于定义在图像上的能量准则，其中区域由其对一组滤波器的响应的非参数分布来表示。在监督情况下，分割算法由区域级统计量和纹理原型之间的相似性度量以及边界上的基于函数的最小化组成，该函数对区域边界施加平滑度和正则性。在无监督情况下，数据驱动项涉及区域之间差异的最大化。所提出的相似性度量是通用的，可以最优地融合各种类型的纹理特征。它定义为特征分布之间的 Kullback-Leibler 散度的加权和。所提出的变分准则的优化是使用水平集公式进行的。与经典活动轮廓方法相比，这种基于区域的公式的有效性和鲁棒性在各种 Brodatz 和自然图像上进行了评估。
