Haliche Zohra, Hammouche Kamal, Losson Olivier, Macaire Ludovic
Laboratoire Vision Artificielle et Automatique des Systèmes, Université Mouloud Mammeri, Tizi-Ouzou 15000, Algeria.
Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France.
J Imaging. 2022 Sep 8;8(9):244. doi: 10.3390/jimaging8090244.
Fuzzy gray-level aura matrices have been developed from fuzzy set theory and the aura concept to characterize texture images. They have proven to be powerful descriptors for color texture classification. However, using them for color texture segmentation is difficult because of their high memory and computation requirements. To overcome this problem, we propose to extend fuzzy gray-level aura matrices to fuzzy color aura matrices, which would allow us to apply them to color texture image segmentation. Unlike the marginal approach that requires one fuzzy gray-level aura matrix for each color channel, a single fuzzy color aura matrix is required to locally characterize the interactions between colors of neighboring pixels. Furthermore, all works about fuzzy gray-level aura matrices consider the same neighborhood function for each site. Another contribution of this paper is to define an adaptive neighborhood function based on information about neighboring sites provided by a pre-segmentation method. For this purpose, we propose a modified simple linear iterative clustering algorithm that incorporates a regional feature in order to partition the image into superpixels. All in all, the proposed color texture image segmentation boils down to a superpixel classification using a simple supervised classifier, each superpixel being characterized by a fuzzy color aura matrix. Experimental results on the Prague texture segmentation benchmark show that our method outperforms the classical state-of-the-art supervised segmentation methods and is similar to recent methods based on deep learning.
模糊灰度光环矩阵是基于模糊集理论和光环概念开发的，用于表征纹理图像。它们已被证明是用于颜色纹理分类的强大描述符。然而，由于其高内存和计算要求，将它们用于颜色纹理分割很困难。为了克服这个问题，我们建议将模糊灰度光环矩阵扩展为模糊颜色光环矩阵，这将使我们能够将它们应用于颜色纹理图像分割。与需要为每个颜色通道使用一个模糊灰度光环矩阵的边缘方法不同，局部表征相邻像素颜色之间的相互作用只需要一个模糊颜色光环矩阵。此外，所有关于模糊灰度光环矩阵的工作都为每个位置考虑相同的邻域函数。本文的另一个贡献是基于预分割方法提供的关于相邻位置的信息定义一个自适应邻域函数。为此，我们提出了一种改进的简单线性迭代聚类算法，该算法结合了区域特征以将图像分割为超像素。总而言之，所提出的颜色纹理图像分割归结为使用简单监督分类器的超像素分类，每个超像素由一个模糊颜色光环矩阵表征。在布拉格纹理分割基准上的实验结果表明，我们的方法优于经典的最先进监督分割方法，并且与最近基于深度学习的方法相似。
