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A New Approach for Unsupervised Classification in Image Segmentation

机译：图像分割中无监督分类的新方法

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摘要

Image segmentation is a fundamental problem in image analysis and understanding. Among the existing approaches proposed to solve this problem, unsupervised classification or clustering is often involved in an early step to partition the space of pixel intensities (i.e. either grey levels, colours or spectral signatures). Since it completely ignores pixel neighbourhoods, a second step is then necessary to ensure spatial analysis (e.g. with a connected component labeling) in order to identify the regions built from the segmentation process. The lack of spatial information is a major drawback of the classification-based segmentation approaches, thus many solutions (where classification is used together with other techniques) have been proposed in the literature. In this paper, we propose a new formulation of the unsupervised classification which is able to perform image segmentation without requiring the need for some additional techniques. More precisely, we introduce a kmeans-like method where data to be clustered are pixels themselves (and not anymore their intensities or colours) and where distances between points and class centres are not anymore Euclidean but topographical. Segmentation is then an iterative process, where at each iteration resulting classes can be seen as influence zones in the context of mathematical morphology. This comparison provides some efficient algorithms proposed in this field (such as hierarchical queue-based solutions), while adding the iterative property of unsupervised classification methods considered here. Finally, we illustrate the potential of our approach by some segmentation results obtained on artificial and natural images.
机译：图像分割是图像分析和理解中的基本问题。在为解决该问题而提出的现有方法中，无监督分类或聚类通常涉及对像素强度空间（即灰度级，颜色或光谱特征）进行划分的早期步骤。由于它完全忽略了像素邻域，因此需要第二步以确保空间分析（例如，使用连接的组件标记），以便识别从分割过程中构建的区域。缺乏空间信息是基于分类的分割方法的主要缺点，因此在文献中提出了许多解决方案（将分类与其他技术一起使用）。在本文中，我们提出了一种无监督分类的新公式，该公式能够执行图像分割而无需其他技术。更准确地说，我们引入了一种类似kmeans的方法，其中要聚类的数据是像素本身（而不是它们的强度或颜色），并且点与类中心之间的距离不再是欧几里得，而是地形。然后，分段是一个迭代过程，其中在每次迭代时，在数学形态学的上下文中，可以将所得的类视为影响区域。这种比较提供了该领域提出的一些有效算法（例如基于分层队列的解决方案），同时增加了此处考虑的无监督分类方法的迭代属性。最后，我们通过在人工和自然图像上获得的一些分割结果来说明该方法的潜力。

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